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Each system has its own unique set of applications, services, and hardware, which generate various types of events and errors that are recorded in logs.

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Welcome to the StormEye Resource page, where we introduce the various log types that our system supports. Logs are a crucial element in identifying and resolving issues, vulnerabilities, and potential cyber threats within an organization. Our system supports a wide range of log types, including system logs, operating system logs, web logs, database logs, infrastructure logs, and custom logs.

System logs contain essential information about an operating system, such as events, errors, and security issues. Operating system logs, on the other hand, provide detailed information about system operations, including user activity, performance, and security issues.

Our system also supports various weblogs, including Apache Web Server, Nginx, HAProxy, and MS IIS, which provide critical information about web traffic, server performance, and user activity.

Our log management system can handle several database logs, such as Elasticsearch, MongoDB, MariaDB, Memcache, Redis, PostgreSQL, MYSQL, and OpenSearch. These logs provide insights into database performance, queries, and potential vulnerabilities.

Our infrastructure logs section supports logs for essential tools such as Apache Kafka, Apache Zookeeper, RabbitMQ, and Fail2ban. These logs provide insights into the performance, errors, and potential security issues within the infrastructure.

We support custom logs, which allow users to create specific log formats for their unique organizational needs. Our log management system provides an easy-to-use platform for managing and analyzing these logs, enabling organizations to identify and address issues quickly and efficiently.

System log

A system log, also known as a syslog, is a record of everything that happens on a computer in other words, all system activities that are stored by operating systems, servers, applications, and other devices. It contains information about events, errors, warnings, and other activities that occur within a system. System logs are an essential component of system administration, providing valuable information to help diagnose and troubleshoot problems that may occur within a system.

System logs are usually created automatically and can be accessed by system administrators, security analysts, and other authorized personnel. They contain a wealth of information that can be used to monitor system performance, detect security breaches, and identify potential vulnerabilities. System logs can be generated by various devices such as firewalls, routers, switches, servers, and applications.

System logs contain various types of data, including timestamps, user IDs, process IDs, IP addresses, error messages, and other system-related information. The data is recorded in a structured format that is easily searchable and can be analyzed to gain insights into system performance and security.

Operating system log

Operating System Log

An operating system log is a record of events and activities that occur on a computer's operating system. These logs capture information such as system errors, warning messages, software updates, and user activities. Analyzing operating system logs can help troubleshoot problems, monitor system performance, and identify security threats.

Window event

The Windows Event Log is a record of all events that occur on a Windows operating system. It contains information about system events, security events, and application events. The log can be used to diagnose issues, monitor system performance, and detect security threats. Windows Event Log is an important source of information for IT professionals and cybersecurity teams to maintain the security of Windows systems.

Windows Event logs can collect a wide variety of system events, including:

Application Log: This log records events logged by applications or programs running on the Windows operating system.
Security Log: This log records events related to security, such as successful or failed logon attempts, resource use, and other security-related events.
System Log: This log records events related to the Windows operating system, such as device driver errors, system crashes, and other system-level events.
Setup Log: This log records events related to the installation of software and hardware on the Windows operating system.
Forwarded Events Log: This log collects events that are forwarded from other computers in the network.

Windows Event logs can be a valuable source of information for troubleshooting system issues, diagnosing errors, and monitoring system performance.

Here are ten common cyber attacks that can be traced through Windows Event Logs:

Malware infections: Malware infections can often be traced through the Application, System, or Security logs, as malware may create new files, modify system files, or attempt to make unauthorized network connections.
Phishing attacks: Phishing attacks may be detected in the Security log, which may contain information about suspicious login attempts or unauthorized access attempts.
Ransomware attacks: Ransomware attacks may be detected in the Application or System logs, as they often involve the creation or modification of files or changes to system settings.
Password attacks: Password attacks may be detected in the Security log, which may contain information about failed login attempts or attempts to access resources using invalid credentials.
Denial of Service (DoS) attacks: DoS attacks may be detected in the System or Application logs, as they often involve the disruption of system services or the creation of excessive network traffic.
Network attacks: Network attacks may be detected in the Security log, which may contain information about unauthorized access attempts, port scans, or other suspicious network activity.
Insider attacks: Insider attacks may be detected in the Security log, which may contain information about unauthorized access attempts, changes to user permissions or account settings, or other suspicious activity.
Data exfiltration: Data exfiltration may be detected in the Security log, which may contain information about unauthorized access attempts or unusual network traffic patterns.
Web application attacks: Web application attacks may be detected in the Application log, which may contain information about unauthorized access attempts, errors or exceptions generated by the application, or other suspicious activity.
SQL injection attacks: SQL injection attacks may be detected in the Application log, which may contain information about errors or exceptions generated by the application when attempting to execute SQL commands.

To study the Windows Event Logs for traces of these attacks, you can use event log analysis tools to do so or you can put selected logs into "Ask AI". This allows you to filter and search for specific event types, such as login failures, file modifications, or network connections and correlate events to identify potential attack patterns. You can also set up alerts or notifications to notify you when specific events occur, such as multiple failed login attempts or suspicious network traffic, apart from setting it in Event viewer, StormEye is going to support custom alert functions in mid 2023.

Linux Syslog

Linux syslog is a system logging protocol used in Linux and other Unix-like operating systems for logging system messages.

Linux Syslog is capable of collecting various logs generated by different services and applications running on a Linux system. Some examples include:

Kernel logs: These logs contain information related to the Linux kernel, including boot messages, device driver messages, and system crashes.
System logs: These logs contain information related to the Linux system, including authentication events, system daemons, and system resource usage.
Application logs: These logs contain information related to the applications installed on the Linux system, including web servers, databases, and mail servers.
Security logs: These logs contain information related to security events, including login attempts, failed authentication events, and changes to system files and directories.
Network logs: These logs contain information related to network events, including firewall logs, DHCP server logs, and DNS server logs.

In general, Linux Syslog can collect logs from a wide range of sources on a Linux system, making it a versatile tool for managing and analyzing logs.

Here are ten common cyber attacks that can be traced through Linux Syslog logs:

Brute force attacks: Check for repeated login failures or authentication attempts from suspicious IP addresses.
Malware infections: Look for unusual processes or files that are created or executed.
DNS attacks: Monitor DNS queries and responses for suspicious domain names or IP addresses.
SQL injections: Check web server logs for unusual or suspicious queries.
DDoS attacks: Monitor network traffic logs for unusually high amounts of traffic or requests.
Unauthorized access: Look for unauthorized login attempts or changes to system files or configurations.
Phishing attacks: Monitor email logs for suspicious or malicious attachments or URLs.
Ransomware attacks: Check for changes to files or file names and unusual network traffic or file transfers.
Insider threats: Monitor employee access and activity logs for suspicious or unauthorized actions.
Data breaches: Look for suspicious access or transfers of sensitive data or large amounts of data.

To trace these attacks via Linux Syslog logs, a log management system can be used to collect and analyze logs from various sources. The system can be configured to generate alerts for specific types of activities or events that are considered suspicious or malicious.

The logs can be analyzed using various techniques such as pattern matching, anomaly detection, or machine learning to identify potential threats or attacks. Once identified, the system can generate alerts or notifications to security teams for further investigation and response.

Web log

Web Log

A weblog, also known as a weblog or simply a "blog," is a type of log file that contains a record of all requests made to a web server. Each request is typically logged in the form of an entry, which contains information about the request, such as the IP address of the client making the request, the time and date of the request, the URL being requested, the HTTP status code returned by the server, and the size of the response.

Web logs are commonly used for website analytics and to monitor website traffic, as they can provide insights into user behaviour and help identify potential issues with the website or server. They can also be used to track and analyze user activity, detect suspicious or malicious traffic, and help with troubleshooting and debugging issues.

Apache web server

The Apache HTTP Server is a popular open-source web server software that can run on various operating systems such as Windows, Linux, and macOS. It is designed to handle a large number of requests and is commonly used for serving web pages and other content on the internet. It offers a wide range of features and modules that allow users to customize the server according to their specific needs. Apache web server produces log files that capture information about server activity, including access logs that record all requests received by the server, error logs that capture any errors encountered during server operation, and other types of logs that provide additional information about server performance and behaviour.

Apache web servers can collect several types of logs, which can provide valuable information for troubleshooting and monitoring web server activity.

Here are some common Apache log types:

Access Log: This log records all requests made to the Apache web server, including the IP address of the client, the date and time of the request, the requested URL, the HTTP status code returned by the server, and the amount of data transferred.
Error Log: This log records any errors or warnings generated by the Apache web server, such as syntax errors in configuration files, server startup errors, or script execution errors.
SSL Request Log: This log records all SSL/TLS requests made to the Apache web server, including the IP address of the client, the date and time of the request, the requested URL, the SSL/TLS protocol version used, and the cipher suite used for encryption.
Rewrite Log: This log records all requests that are processed by the Apache URL rewriting engine, which allows you to modify URLs based on pattern-matching rules.
Proxy Log: This log records information about requests forwarded to a backend server by the Apache proxy server, including the IP address of the backend server, the date and time of the request, and the request method and URL.
Custom Log: Apache also allows you to define custom log formats, which can include any combination of server variables, such as the user agent string, the referrer URL, or custom application-specific variables.

Analyzing Apache logs can provide insights into web server performance, user behaviour, and potential security threats. You can use tools like Apache Log Viewer, to analyze, and visualize Apache logs. Additionally, you can configure log rotation and archiving to manage the size and retention of Apache logs.

Here are ten common cyber attacks that can be traced through Apache web server logs:

Brute force attacks: Brute force attacks can be detected by studying the Access log, which may contain a high number of failed login attempts to a specific URL or resource.
Cross-site scripting (XSS) attacks: XSS attacks can be detected by studying the Access log for requests that contain suspicious or unexpected characters or scripts in the URL or request parameters.
SQL injection attacks: SQL injection attacks can be detected by studying the Access log for requests that contain suspicious or unexpected SQL statements in the URL or request parameters.
Directory traversal attacks: Directory traversal attacks can be detected by studying the Access log for requests that contain "../" or other malicious directory traversal strings in the URL.
Denial of Service (DoS) attacks: DoS attacks can be detected by studying the Access log for a high number of requests to a specific URL or resource within a short period of time.
File inclusion attacks: File inclusion attacks can be detected by studying the Access log for requests that include unexpected or malicious files in the URL or request parameters.
Malware attacks: Malware attacks can be detected by studying the Access log for requests that download or execute suspicious files, such as .exe or .bat files.
Phishing attacks: Phishing attacks can be detected by studying the Access log for requests that attempt to steal user credentials or other sensitive information.
Remote file inclusion attacks: Remote file inclusion attacks can be detected by studying the Access log for requests that include external files or scripts in the URL or request parameters.
Server misconfiguration attacks: Server misconfiguration attacks can be detected by studying the Error log for messages related to syntax errors or other configuration issues.

To study Apache web server logs for traces of these attacks, you can use log analysis tools like Apache Log Viewer. This tool allows you to filter and search for specific log entries, such as failed login attempts, suspicious URLs, or unusual request patterns. You can also set up alerts or notifications to notify you when specific events occur, such as a high number of requests to a specific URL or resource within a short period of time. Additionally, you can configure log rotation and archiving to manage the size and retention of Apache logs.

Nginx

Nginx is an open-source web server software that is known for its high performance, stability, and low resource consumption. It is designed to handle a large number of simultaneous connections and has become popular as a reverse proxy server for load balancing, HTTP caching, and SSL/TLS encryption. Nginx can also function as a web server, proxy server, and reverse proxy server for HTTP, HTTPS, SMTP, POP3, and IMAP protocols. It is widely used by popular websites such as Netflix, Airbnb, Dropbox, and WordPress.

Nginx web server can collect several types of logs, which can provide valuable information for troubleshooting and monitoring web server activity. Here are some common Nginx log types:

Access Log: This log records all requests made to the Nginx web server, including the IP address of the client, the date and time of the request, the requested URL, the HTTP status code returned by the server, and the amount of data transferred.
Error Log: This log records any errors or warnings generated by the Nginx web server, such as syntax errors in configuration files, server startup errors, or script execution errors.
Rewrite Log: This log records all requests that are processed by the Nginx URL rewriting engine, which allows you to modify URLs based on pattern-matching rules.
SSL Request Log: This log records all SSL/TLS requests made to the Nginx web server, including the IP address of the client, the date and time of the request, the requested URL, the SSL/TLS protocol version used, and the cipher suite used for encryption.
Proxy Log: This log records information about requests forwarded to a backend server by the Nginx proxy server, including the IP address of the backend server, the date and time of the request, and the request method and URL.
Cache Log: This log records information about the Nginx caching system, including cache hits and misses, cache expiration times, and cache sizes.

Analyzing Nginx logs can provide insights into web server performance, user behaviour, and potential security threats. You can use tools like GoAccess, Logwatch, or Logrotate to parse, analyze, and visualize Nginx logs. Additionally, you can configure log rotation and archiving to manage the size and retention of Nginx logs.

HAProxy

HAProxy is a free and open-source load-balancing software that can distribute traffic across multiple servers or resources. It is commonly used as a reverse proxy and can handle a variety of protocols, including HTTP, TCP, and UDP. HAProxy is known for its high performance and stability, as well as its ability to scale horizontally to handle increasing amounts of traffic. It supports multiple load-balancing algorithms and can perform health checks to ensure that requests are only forwarded to healthy servers. HAProxy is widely used in production environments for high-traffic websites and applications that require high availability and reliability.

HAProxy is a popular open-source load balancer and proxy server that can collect several types of logs, including:

Access logs: These logs record all requests made to the server, including client IP addresses, requested URLs, and server responses.
Error logs: These logs contain information about any errors or problems encountered during the operation of the server.
TCP logs: These logs capture information about the network connections made to and from the server.
HTTP logs: These logs provide detailed information about the HTTP requests and responses processed by the server, including headers, status codes, and message bodies.
Health check logs: These logs record the results of the periodic health checks performed by the load balancer to ensure that the backend servers are functioning correctly.
SSL logs: These logs contain information about the SSL/TLS connections established by the server, including the SSL/TLS version, cipher suite, and certificate information.
Debug logs: These logs provide additional diagnostic information that can be useful for troubleshooting issues with the server.

The specific types of logs collected by HAProxy can be configured and customized based on the needs of the user.

Here are 10 common cyber attacks that can be traced via HAProxy log analysis:

DDoS attacks - By analyzing the number of incoming requests per second or minute, it is possible to identify whether a DDoS attack is underway.
SQL injection attacks - HAProxy can log HTTP requests, and by examining the HTTP headers and payloads, it is possible to detect SQL injection attempts.
Cross-site scripting (XSS) attacks - Similar to SQL injection attacks, XSS attacks can be identified by analyzing the HTTP headers and payloads.
Directory traversal attacks - By monitoring requests to web servers and analyzing file paths, it is possible to identify directory traversal attacks.
Brute-force attacks - By examining the number of login attempts made within a certain time period, it is possible to identify brute-force attacks.
Malware downloads - By monitoring HTTP responses and analyzing the file types and sizes, it is possible to identify malware downloads.
Botnet attacks - By analyzing the user-agent strings in HTTP requests, it is possible to identify botnet attacks.
Unauthorized access attempts - By analyzing the number of requests to restricted URLs, it is possible to detect attempts at unauthorized access.
Exploits targeting web server vulnerabilities - By analyzing HTTP requests and payloads, it is possible to identify attempts at exploiting known vulnerabilities.
Credential stuffing attacks - By analyzing login attempts and patterns, it is possible to identify credential stuffing attacks.

To trace these cyber attacks, log data from HAProxy needs to be collected, parsed and analyzed using a StormEye or other SIEM tool. Relevant data fields and filters need to be applied to extract meaningful insights from the log data.

MS IIS

Microsoft Internet Information Services (IIS) is a web server software application developed by Microsoft. It is used to host and manage websites and other web-based applications on Windows servers. MS IIS supports a variety of technologies such as ASP.NET, PHP, and Microsoft's own .NET framework. It provides a range of features including SSL encryption, virtual hosting, FTP, logging, and authentication. MS IIS is widely used by organizations to host and manage their websites and web applications due to its security features and ease of use.

MS IIS is capable of generating various types of logs to capture important information about the web server’s activity, such as access logs, error logs, and FTP logs. Some of the common types of logs generated by MS IIS include:

HTTP request logs: These logs capture information about HTTP requests made to the server, including the source IP address, the request method, the requested resource, and the user agent.
Error logs: These logs record information about errors that occur on the server, such as 404 errors and other HTTP status codes.
FTP logs: These logs record information about FTP sessions, including successful and failed login attempts, file uploads and downloads, and other FTP commands.
Application logs: These logs capture information about the server’s applications, such as ASP.NET applications, including error messages, trace information, and other application-specific events.

By analyzing MS IIS logs, organizations can identify various types of cyberattacks, such as:

SQL Injection: Attackers can use SQL injection attacks to manipulate the SQL queries made to the server and potentially gain unauthorized access to sensitive data.
Cross-Site Scripting (XSS): Attackers can use XSS attacks to inject malicious code into web pages, potentially compromising users who access those pages.
Brute force attacks: Attackers can use brute force attacks to guess login credentials for the server or individual accounts.
DDoS attacks: Attackers can launch DDoS attacks against web servers, overwhelming them with traffic and causing them to become unavailable.
File Inclusion Attacks: Attackers can use file inclusion attacks to execute arbitrary code on the server and potentially gain unauthorized access to sensitive data.

To detect and respond to these types of attacks, organizations can use log analysis tools that can parse and analyze MS IIS logs in real time, alerting security teams to suspicious activity and providing insights into the source and nature of the attack.

Database log

A database log is a file that records all the activities that occur in a database management system (DBMS) or a specific database. The log keeps a record of every change made to the data, including additions, modifications, and deletions. It can also record events such as backup and recovery operations, user logins and logouts, and system errors.

The purpose of a database log is to provide a detailed record of all activities within a database. This record can be used for troubleshooting, auditing, and recovery purposes. In the event of a system failure or a security breach, the log can be used to identify the cause of the problem and restore the database to its previous state.

Database logs are essential for ensuring data integrity, maintaining data security, and complying with regulatory requirements. They are often used in conjunction with other log types, such as system logs and application logs, to provide a comprehensive view of the entire IT infrastructure.

Elasticsearch

Elasticsearch is a distributed, open-source search and analytics engine designed for handling large volumes of structured and unstructured data. It is built on top of the Apache Lucene library and provides a scalable and reliable platform for storing, searching and analyzing data in real time.

Elasticsearch can collect various types of logs, including:

Application logs: Logs generated by applications, such as web servers, databases, and custom applications.
System logs: Logs generated by the operating system, including information on system events, kernel messages, and system crashes.
Security logs: Logs generated by security tools and platforms, such as firewalls, intrusion detection systems, and antivirus software.
Audit logs: Logs that track user activity and changes to system configurations, providing visibility into who did what, when, and where.

Elasticsearch is designed to be flexible and can handle a wide variety of log formats, including JSON, CSV, and syslog. It is also capable of handling large volumes of logs in real-time, making it an ideal choice for organizations looking to centralize their logging infrastructure. By collecting and analyzing logs in Elasticsearch, organizations can gain valuable insights into system performance, user activity, and potential security threats.

Here are 10 common cyber attacks that can be traced by studying Elasticsearch logs:

Brute force attacks: Studying Elasticsearch logs can help identify brute force attacks on user accounts, as these attacks often result in a large number of failed login attempts.
SQL injection attacks: Elasticsearch logs can show SQL queries that are executed by applications, and abnormal query patterns may indicate SQL injection attacks.
Cross-site scripting (XSS) attacks: Elasticsearch logs can reveal XSS attacks by showing user inputs that are not properly sanitized or escaped.
Denial of Service (DoS) attacks: Elasticsearch logs can help identify DoS attacks by showing an unusually high number of requests or connections from a single IP address or user.
Distributed Denial of Service (DDoS) attacks: Elasticsearch logs can be used to detect DDoS attacks by showing a sudden surge in traffic or requests from multiple IP addresses.
Data exfiltration attempts: Elasticsearch logs can reveal data exfiltration attempts by showing unauthorized access to sensitive data or abnormal data transfer patterns.
Malware infections: Elasticsearch logs can be used to detect malware infections by showing suspicious activity, such as unauthorized network connections or file modifications.
Account takeover attacks: Elasticsearch logs can help identify account takeover attacks by showing login attempts from unfamiliar locations or devices.
Insider threats: Elasticsearch logs can reveal insider threats by showing abnormal user activity or unauthorized access attempts.
File integrity violations: Elasticsearch logs can help detect file integrity violations by showing modifications or deletions of critical system files.

To trace these cyber attacks via Elasticsearch logs, one can use log analysis tools which combine Elasticsearch, Logstash, and Kibana to ingest, process, and visualize logs. By analyzing Elasticsearch logs in real time, organizations can proactively detect and respond to cyber threats, enhancing their overall cybersecurity posture.

MongoDB

MongoDB is a popular NoSQL document-oriented database management system (DBMS). Instead of storing data in rows and columns like a traditional relational database, MongoDB stores data in flexible, JSON-like documents with dynamic schemas. This allows for more flexible and scalable data models, as well as faster development and deployment of applications.

MongoDB is known for its ease of use and scalability, making it a popular choice for web and mobile applications, content management systems, and data analytics platforms.

MongoDB is a NoSQL database that can collect several types of logs, which can provide valuable information for troubleshooting and monitoring database activity. Here are some common MongoDB log types:

MongoDB Log: This log contains general information about MongoDB database activity, such as server startup and shutdown, client connections, and database commands executed.
Query Log: This log records all queries made to the MongoDB database, including the query syntax, query parameters, and execution time.
Replica Set Log: This log records information about MongoDB replica set activity, such as changes to the primary and secondary nodes, and replication lag.
Sharding Log: This log records information about MongoDB sharding activity, such as data migrations between shards and shard key changes.
Slow Query Log: This log records MongoDB queries that exceed a certain execution time threshold, which can help identify performance issues.
System Log: This log records system events related to the MongoDB database, such as disk space usage, network activity, and memory usage.

Analyzing MongoDB logs can provide insights into database performance, query optimization, and potential security threats. You can use tools like MongoDB Compass, MongoDB Atlas, or the MongoDB Shell to view and analyze MongoDB logs. Additionally, you can configure log rotation and archiving to manage the size and retention of MongoDB logs.

Here are 10 common cyber attacks that can be traced by studying MongoDB logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the MongoDB log.
Injection attacks: These attacks involve inserting malicious code into MongoDB queries or data. They can be detected by monitoring unusual query patterns or unexpected data inputs in the MongoDB log.
Malware attacks: These attacks involve installing malware on a MongoDB server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the MongoDB log.
DDoS attacks: These attacks involve flooding a MongoDB server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high query volumes in the MongoDB log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in MongoDB access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the MongoDB log.
Ransomware attacks: These attacks involve encrypting data on a MongoDB server and demanding payment for the decryption key. They can be detected by monitoring unusual file access or modification in the MongoDB log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into MongoDB data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or query outputs in the MongoDB log.
Data exfiltration attacks: These attacks involve stealing data from a MongoDB server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or file transfers in the MongoDB log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying MongoDB network traffic to steal data or execute malicious queries. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the MongoDB log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a MongoDB server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or access patterns in the MongoDB log.

To detect these cyber attacks in MongoDB logs, you can use log analysis tools like MongoDB Compass, MongoDB Atlas, or MongoDB Shell. Look for patterns of unusual activity, such as high query volumes, failed logins, or unexpected data inputs. You can also configure MongoDB to log at a high level of verbosity to capture more detailed information about database activity. Additionally, you can set up alerts or triggers to notify you of suspicious or anomalous activity in real-time.

MariaDB

MariaDB is an open-source relational database management system (RDBMS) and a community-driven fork of the MySQL database system. MariaDB was created in response to concerns around the acquisition of MySQL by Oracle Corporation and its subsequent development and licensing direction.

MariaDB retains many of the features of MySQL while also adding new functionality and performance improvements.

MariaDB is a popular open-source relational database management system that can collect several types of logs. Here are some common MariaDB log types:

General Query Log: This log records all queries made to the MariaDB database, including the query syntax, query parameters, and execution time.
Error Log: This log records errors and warnings that occur during MariaDB server operation, including startup and shutdown issues, syntax errors, and database corruption.
Slow Query Log: This log records MariaDB queries that exceed a certain execution time threshold, which can help identify performance issues.
Binary Log: This log records all modifications made to the MariaDB database, including insertions, deletions, and updates, in a binary format for replication purposes.
Binary Log Index: This log is an index of the binary log that facilitates fast queries and lookups.
InnoDB Log Files: These logs record InnoDB transactions, including information about committed and rolled-back transactions, as well as any errors or warnings that occur.
System Log: This log records system events related to the MariaDB database, such as disk space usage, network activity, and memory usage.

Analyzing MariaDB logs can provide insights into database performance, query optimization, and potential security threats. Here are 10 common cyber attacks that can be traced via studying MariaDB logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the MariaDB log.
Injection attacks: These attacks involve inserting malicious code into MariaDB queries or data. They can be detected by monitoring unusual query patterns or unexpected data inputs in the MariaDB log.
Malware attacks: These attacks involve installing malware on a MariaDB server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the MariaDB log.
DDoS attacks: These attacks involve flooding a MariaDB server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high query volumes in the MariaDB log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in MariaDB access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the MariaDB log.
Ransomware attacks: These attacks involve encrypting data on a MariaDB server and demanding payment for the decryption key. They can be detected by monitoring unusual file access or modification in the MariaDB log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into MariaDB data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or query outputs in the MariaDB log.
Data exfiltration attacks: These attacks involve stealing data from a MariaDB server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or file transfers in the MariaDB log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying MariaDB network traffic to steal data or execute malicious queries. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the MariaDB log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a MariaDB server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or access patterns in the MariaDB log.

To detect these cyber attacks in MariaDB logs, you can use log analysis tools like the MariaDB log analyzer or other third-party log analysis tools. Look for patterns of unusual activity, such as high query volumes, failed logins, or unexpected data inputs. You can also configure MariaDB

MySQL

MYSQL

MySQL is an open-source relational database management system (RDBMS) that is widely used for web applications, particularly those that rely on PHP. It is owned by Oracle Corporation.

MySQL is a powerful and reliable database system that can handle large amounts of data and can be used to manage complex and critical applications. It uses SQL (Structured Query Language) to manage data in relational databases and supports a wide range of features including transactions, indexing, and replication.

MySQL is a popular open-source relational database management system that can collect several types of logs. Here are some common MySQL log types:

General Query Log: This log records all queries made to the MySQL database, including the query syntax, query parameters, and execution time.
Error Log: This log records errors and warnings that occur during MySQL server operation, including startup and shutdown issues, syntax errors, and database corruption.
Slow Query Log: This log records MySQL queries that exceed a certain execution time threshold, which can help identify performance issues.
Binary Log: This log records all modifications made to the MySQL database, including insertions, deletions, and updates, in a binary format for replication purposes.
Binary Log Index: This log is an index of the binary log that facilitates fast queries and lookups.
InnoDB Log Files: These logs record InnoDB transactions, including information about committed and rolled-back transactions, as well as any errors or warnings that occur.
System Log: This log records system events related to the MySQL database, such as disk space usage, network activity, and memory usage.

Analyzing MySQL logs can provide insights into database performance, query optimization, and potential security threats. Here are 10 common cyber attacks that can be traced via studying MySQL logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the MySQL log.
Injection attacks: These attacks involve inserting malicious code into MySQL queries or data. They can be detected by monitoring unusual query patterns or unexpected data inputs in the MySQL log.
Malware attacks: These attacks involve installing malware on a MySQL server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the MySQL log.
DDoS attacks: These attacks involve flooding a MySQL server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high query volumes in the MySQL log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in MySQL access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the MySQL log.
Ransomware attacks: These attacks involve encrypting data on a MySQL server and demanding payment for the decryption key. They can be detected by monitoring unusual file access or modification in the MySQL log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into MySQL data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or query outputs in the MySQL log.
Data exfiltration attacks: These attacks involve stealing data from a MySQL server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or file transfers in the MySQL log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying MySQL network traffic to steal data or execute malicious queries. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the MySQL log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a MySQL server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or access patterns in the MySQL log.

To detect these cyber attacks in MySQL logs, you can use log analysis tools like the MySQL log analyzer or other third-party log analysis tools. Look for patterns of unusual activity, such as high query volumes, failed logins, or unexpected data inputs. You can also configure MySQL logging options to increase the granularity of log data for better analysis.

PostgreSQL

PostgreSQL, also known as Postgres, is an open-source relational database management system (RDBMS) that is widely used for web applications and other data-driven applications. It is designed to be highly scalable, reliable, and secure, and is known for its robustness and feature-richness.

PostgreSQL uses SQL (Structured Query Language) to manage data in relational databases and supports a wide range of advanced features such as indexing, transactions, and replication. It is also known for its support of advanced data types, such as arrays, hstore, and JSON.

PostgreSQL is a popular open-source relational database management system that can collect several types of logs. Here are some common PostgreSQL log types:

Error Log: This log records errors and warnings that occur during PostgreSQL server operation, including startup and shutdown issues, syntax errors, and database corruption.
Query Log: This log records all queries made to the PostgreSQL database, including the query syntax, query parameters, and execution time.
Slow Query Log: This log records PostgreSQL queries that exceed a certain execution time threshold, which can help identify performance issues.
Transaction Log: This log records all changes made to the PostgreSQL database, including insertions, deletions, and updates, in a transactional format for replication and recovery purposes.
WAL (Write-Ahead Log) Log: This log records changes made to the PostgreSQL database in a binary format for replication and recovery purposes.
Access Log: This log records all client connections and disconnections to the PostgreSQL database, including the source IP address, username, and authentication method.
Replication Log: This log records replication activity between PostgreSQL servers, including replication slot creation and deletion, streaming and logical replication, and replication status updates.
Autovacuum Log: This log records PostgreSQL's automatic vacuum and analyze processes, which help maintain database performance and stability.
Authentication Log: This log records authentication-related events, including successful and failed login attempts, user account management, and password changes.
System Log: This log records system events related to the PostgreSQL database, such as disk space usage, network activity, and memory usage.

Analyzing PostgreSQL logs can provide insights into database performance, query optimization, and potential security threats. Here are 10 common cyber attacks that can be traced via studying PostgreSQL logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the PostgreSQL access log.
Injection attacks: These attacks involve inserting malicious code into PostgreSQL queries or data. They can be detected by monitoring unusual query patterns or unexpected data inputs in the PostgreSQL query log.
Malware attacks: These attacks involve installing malware on a PostgreSQL server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the PostgreSQL system log.
DDoS attacks: These attacks involve flooding a PostgreSQL server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high query volumes in the PostgreSQL query log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in PostgreSQL access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the PostgreSQL access log.
Ransomware attacks: These attacks involve encrypting data on a PostgreSQL server and demanding payment for the decryption key. They can be detected by monitoring unusual file access or modification in the PostgreSQL transaction log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into PostgreSQL data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or query outputs in the PostgreSQL query log.
Data exfiltration attacks: These attacks involve stealing data from a PostgreSQL server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or file transfers in the PostgreSQL system log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying PostgreSQL network traffic to steal data or execute malicious queries. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the PostgreSQL system log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a PostgreSQL server abusing their privileges for personal gain or malicious purposes.

Redis

Redis is an open-source, in-memory data structure store that is used as a database, cache, and message broker. It is known for its high performance and versatility, and is often used in web applications and real-time systems that require fast and efficient data processing.

Redis is designed to be flexible and support a wide range of data structures, including strings, hashes, sets, lists, and sorted sets. It also supports advanced features such as transactions, pub/sub messaging, Lua scripting, and clustering.

Redis is an in-memory data store that can collect several types of logs. Here are some common Redis log types:

Server Log: This log records server events related to Redis, such as startup and shutdown, configuration changes, and background tasks.
Client Log: This log records client connections and disconnections to Redis, including the client IP address, username, and authentication method.
Slow Log: This log records Redis commands that exceed a certain execution time threshold, which can help identify performance issues.
Error Log: This log records Redis errors and warnings, including syntax errors, resource limitations, and memory allocation issues.
Command Log: This log records all Redis commands made by clients, including the command syntax, command parameters, and execution time.
Replication Log: This log records replication activity between Redis servers, including replication status updates, failover events, and slave synchronization.
Sentinel Log: This log records events related to Redis Sentinel, a tool for high availability and failover management in Redis clusters.
Lua Script Log: This log records events related to Redis Lua scripts, including script execution errors, resource limitations, and memory allocation issues.
Eviction Log: This log records events related to Redis key eviction, a process for freeing memory by removing least recently used keys.
Persistence Log: This log records events related to Redis persistence, a process for saving Redis data to disk for backup and recovery purposes.

Analyzing Redis logs can provide insights into performance optimization, replication management, and potential security threats.

Here are 10 common cyber attacks that can be traced via studying Redis logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the Redis client log.
Injection attacks: These attacks involve inserting malicious code into Redis commands or data. They can be detected by monitoring unusual command patterns or unexpected data inputs in the Redis command log.
Malware attacks: These attacks involve installing malware on a Redis server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the Redis server log.
DDoS attacks: These attacks involve flooding a Redis server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high command volumes in the Redis command log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in Redis access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the Redis client log.
Ransomware attacks: These attacks involve encrypting data on a Redis server and demanding payment for the decryption key. They can be detected by monitoring unusual key access or modification in the Redis command log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into Redis data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or command outputs in the Redis command log.
Data exfiltration attacks: These attacks involve stealing data from a Redis server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or command outputs in the Redis server log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying Redis network traffic to steal data or execute malicious commands. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the Redis server log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a Redis server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or command patterns in the Redis client log.

Memached

Memcached

Memcached is an open-source, distributed memory caching system that is used to speed up dynamic web applications by reducing the load on database servers. It is designed to store and retrieve key-value pairs in memory, allowing frequently accessed data to be served quickly and efficiently.

Memcached is often used in large-scale web applications that require high-speed, low-latency access to data, such as social media platforms, e-commerce websites, and gaming applications. By caching frequently accessed data in memory,

Memcached can reduce the number of requests made to the database server, resulting in faster page load times and improved performance.

Here are some common Memcached log types:

General Log: This log records general information about Memcached operations, such as startup and shutdown, configuration changes, and memory usage.
Command Log: This log records all Memcached commands made by clients, including the command syntax, command parameters, and execution time.
Access Log: This log records client connections and disconnections to Memcached, including the client IP address, username, and authentication method.
Error Log: This log records Memcached errors and warnings, including syntax errors, resource limitations, and memory allocation issues.
Replication Log: This log records replication activity between Memcached servers, including replication status updates, failover events, and synchronization.

Analyzing Memcached logs can provide insights into performance optimization, replication management, and potential security threats. Here are 10 common cyber attacks that can be traced via studying Memcached logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the Memcached access log.
Injection attacks: These attacks involve inserting malicious code into Memcached commands or data. They can be detected by monitoring unusual command patterns or unexpected data inputs in the Memcached command log.
Malware attacks: These attacks involve installing malware on a Memcached server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the Memcached general log.
DDoS attacks: These attacks involve flooding a Memcached server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high command volumes in the Memcached command log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in Memcached access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the Memcached access log.
Ransomware attacks: These attacks involve encrypting data on a Memcached server and demanding payment for the decryption key. They can be detected by monitoring unusual key access or modification in the Memcached command log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into Memcached data that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or command outputs in the Memcached command log.
Data exfiltration attacks: These attacks involve stealing data from a Memcached server and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or command outputs in the Memcached general log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying Memcached network traffic to steal data or execute malicious commands. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the Memcached general log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to a Memcached server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or command patterns in the Memcached access log.

Opensearch

OpenSearch

OpenSearch is a search engine software that allows users to index and search data stored in various sources, such as databases, websites, and file systems. It is a fork of Elasticsearch, a popular open-source search and analytics engine.

OpenSearch is designed to be flexible, scalable, and efficient, and it can be used for a wide range of use cases, such as e-commerce search, log analytics, and content management. OpenSearch supports a variety of data formats and protocols, including JSON, REST, and SQL, and it provides a powerful query language and a range of search features, such as faceted search, geo-search, and autocomplete. OpenSearch is released under the Apache License 2.0 and is maintained by a community of developers and organizations.

OpenSearch is a popular search and analytics engine that can collect several types of logs. Here are some common OpenSearch log types:

General Log: This log records general information about OpenSearch operations, such as startup and shutdown, configuration changes, and resource usage.
Indexing Log: This log records information about documents being indexed into OpenSearch, including the document's metadata, contents, and index location.
Query Log: This log records information about user queries submitted to OpenSearch, including the search terms, filters, and response times.
Error Log: This log records OpenSearch errors and warnings, including syntax errors, resource limitations, and index corruption issues.
Access Log: This log records client connections and disconnections to OpenSearch, including the client's IP address, username, and authentication method.

Analyzing OpenSearch logs can provide insights into search performance optimization, index management, and potential security threats. Here are 10 common cyber attacks that can be traced via studying OpenSearch logs:

Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token. They can be detected by monitoring failed login attempts in the OpenSearch access log.
Injection attacks: These attacks involve inserting malicious code into search queries or index documents. They can be detected by monitoring unusual query patterns or unexpected data inputs in the OpenSearch query and indexing logs.
Malware attacks: These attacks involve installing malware on an OpenSearch server to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the OpenSearch general log.
DDoS attacks: These attacks involve flooding an OpenSearch server with traffic to overload it and make it unavailable. They can be detected by monitoring unusual network activity or high query volumes in the OpenSearch query log.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in OpenSearch access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the OpenSearch access log.
Ransomware attacks: These attacks involve encrypting data in OpenSearch indices and demanding payment for the decryption key. They can be detected by monitoring unusual index access or modification in the OpenSearch indexing log.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into OpenSearch query results that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or query outputs in the OpenSearch query log.
Data exfiltration attacks: These attacks involve stealing data from OpenSearch indices and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network activity or query outputs in the OpenSearch query log.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying OpenSearch network traffic to steal data or execute malicious queries. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the OpenSearch general log.
Malicious insiders: These attacks involve employees or contractors with legitimate access to an OpenSearch server abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or query patterns in the OpenSearch access and query logs.

Infrastructure

Infrastructure log

Infrastructure logs are the records generated by various components of an IT infrastructure, including servers, network devices, storage systems, and applications. Infrastructure logs can include system logs, performance metrics, error messages, security events, and other types of data that can help administrators monitor, troubleshoot, and optimize their infrastructure. Infrastructure logs are critical for maintaining the reliability, availability, and security of IT systems, and they are often used for tasks such as performance tuning, capacity planning, and incident response.

Infrastructure logs can be collected and analyzed using various tools and techniques, such as log management platforms, SIEM (Security Information and Event Management) systems, and machine learning algorithms.

Apache Kafka

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. Kafka was originally developed at LinkedIn and later became a part of the Apache Software Foundation. It is designed to handle large amounts of data streams in real-time, and provide high-throughput and low-latency data processing.

Apache Kafka is a popular distributed streaming platform that can collect various types of logs related to its operations. Here are some common Kafka log types:

Controller Log: This log records information about Kafka cluster management, including leader election, partition reassignment, and broker failure detection.
Broker Log: This log records information about Kafka broker operations, including message processing, network connections, and resource usage.
Producer Log: This log records information about messages produced by Kafka producers, including message payloads, delivery times, and acknowledgement statuses.
Consumer Log: This log records information about messages consumed by Kafka consumers, including message offsets, partition assignments, and lag.
ZooKeeper Log: This log records information about ZooKeeper, a distributed coordination service used by Kafka for cluster management, including session expiration, node status, and configuration changes.

Analyzing Kafka logs can provide insights into stream processing optimization, broker management, and potential security threats. Here are 10 common cyber attacks that can be traced via studying Kafka logs:

Data tampering attacks: These attacks involve modifying messages in transit or in storage to manipulate or corrupt the data. They can be detected by monitoring unusual message contents or checksum failures in the Kafka producer and broker logs.
Brute force attacks: These attacks involve repeatedly attempting to guess a user's password or authentication token to gain unauthorized access. They can be detected by monitoring failed login attempts in the Kafka controller and broker logs.
Denial of service (DoS) attacks: These attacks involve flooding a Kafka broker or consumer with traffic to overload it and make it unavailable. They can be detected by monitoring high network traffic or request volumes in the Kafka broker and consumer logs.
Malware attacks: These attacks involve installing malware on a Kafka broker or consumer to steal data or disrupt operations. They can be detected by monitoring unusual network or disk activity in the Kafka broker and consumer logs.
Privilege escalation attacks: These attacks involve exploiting vulnerabilities in Kafka access controls to gain elevated privileges. They can be detected by monitoring unusual user activity or access patterns in the Kafka controller and broker logs.
Cross-site scripting (XSS) attacks: These attacks involve inserting malicious scripts into Kafka message payloads that can execute in a user's browser. They can be detected by monitoring unexpected data inputs or message contents in the Kafka producer and broker logs.
Buffer overflow attacks: These attacks involve exploiting vulnerabilities in Kafka message processing to execute arbitrary code. They can be detected by monitoring unusual message contents or broker crashes in the Kafka broker log.
Data exfiltration attacks: These attacks involve stealing data from Kafka topics and transferring it to an attacker-controlled system. They can be detected by monitoring unusual network or disk activity in the Kafka broker and consumer logs.
Man-in-the-middle (MitM) attacks: These attacks involve intercepting and modifying Kafka network traffic to steal data or execute malicious operations. They can be detected by monitoring unusual network activity or SSL/TLS handshake failures in the Kafka broker and consumer logs.
Malicious insiders: These attacks involve employees or contractors with legitimate access to Kafka systems abusing their privileges for personal gain or malicious purposes. They can be detected by monitoring unusual user activity or message patterns in the Kafka controller and broker logs.

Apache Zookeeper

Apache ZooKeeper is an open-source distributed coordination service that provides a centralized repository for managing configuration information, naming, synchronization, and group services in a distributed system. ZooKeeper allows distributed applications to coordinate and share information with each other in a reliable, fault-tolerant manner. It provides a hierarchical namespace, similar to a file system, where data can be stored and accessed by applications. ZooKeeper also provides notifications and event processing services to inform distributed applications about changes in the system.

Apache ZooKeeper is an open-source distributed coordination service that enables the synchronization of various distributed applications. Here are some of the logs that Apache ZooKeeper can collect:

ZooKeeper server logs: These logs provide information about the server startup, configuration, and shutdown events.
ZooKeeper transaction logs: These logs record every transaction that occurs in the ZooKeeper cluster.
ZooKeeper audit logs: These logs track all the actions performed on ZooKeeper nodes, such as read and write operations, permission changes, and user logins.
ZooKeeper client logs: These logs capture the details of client requests, such as connection establishment, session creation, and session expiration.
ZooKeeper snapshot logs: These logs capture the current state of the ZooKeeper cluster, including the data stored in the nodes and their associated metadata.

Here are ten common cyber attacks that can be traced by studying Apache ZooKeeper logs:

Brute force attacks: These attacks involve multiple login attempts using different username and password combinations. Such attacks can be traced by studying the ZooKeeper audit logs.
Distributed Denial of Service (DDoS) attacks: These attacks involve overwhelming the ZooKeeper cluster with a high volume of traffic. Such attacks can be traced by studying the ZooKeeper server and client logs.
Man-in-the-middle (MITM) attacks: These attacks involve intercepting the communication between the ZooKeeper server and client. Such attacks can be traced by studying the ZooKeeper client logs.
SQL injection attacks: These attacks involve injecting malicious SQL code into the ZooKeeper queries. Such attacks can be traced by studying the ZooKeeper audit logs.
Cross-site scripting (XSS) attacks: These attacks involve injecting malicious scripts into web pages served by ZooKeeper. Such attacks can be traced by studying the ZooKeeper client logs.
Zero-day attacks: These attacks exploit unknown vulnerabilities in the ZooKeeper software. Such attacks can be traced by studying the ZooKeeper server logs.
Privilege escalation attacks: These attacks involve elevating user privileges to gain unauthorized access to the ZooKeeper cluster. Such attacks can be traced by studying the ZooKeeper audit logs.
Malware attacks: These attacks involve deploying malware in the ZooKeeper cluster. Such attacks can be traced by studying the ZooKeeper server logs.
Session hijacking attacks: These attacks involve stealing the session ID of an authenticated user to gain unauthorized access to the ZooKeeper cluster. Such attacks can be traced by studying the ZooKeeper client logs.
Data theft attacks: These attacks involve stealing sensitive data stored in the ZooKeeper nodes. Such attacks can be traced by studying the ZooKeeper snapshot logs.

RabbitMQ

RabbitMQ is an open-source message-broker software that provides a way to send and receive messages between distributed applications. It is based on the Advanced Message Queuing Protocol (AMQP), which is a standardized messaging protocol that enables communication between different applications regardless of the programming language or technology used.

RabbitMQ is an open-source message broker software that is used to send and receive messages between applications. Here are some of the logs that RabbitMQ can collect:

RabbitMQ server logs: These logs provide information about the server startup, configuration, and shutdown events.
RabbitMQ channel logs: These logs capture the details of channel creation, channel close events, and channel errors.
RabbitMQ connection logs: These logs provide details about the client connections to the RabbitMQ server, including connection open and close events.
RabbitMQ queue logs: These logs capture the details of queue creation, queue deletion, and queue message delivery events.
RabbitMQ exchange logs: These logs provide details about exchange creation, exchange deletion, and exchange message routing events.

Here are ten common cyber attacks that can be traced by studying RabbitMQ logs:

Brute force attacks: These attacks involve multiple login attempts using different username and password combinations. Such attacks can be traced by studying the RabbitMQ connection logs.
Distributed Denial of Service (DDoS) attacks: These attacks involve overwhelming the RabbitMQ server with a high volume of traffic. Such attacks can be traced by studying the RabbitMQ server logs.
Man-in-the-middle (MITM) attacks: These attacks involve intercepting the communication between the RabbitMQ server and client. Such attacks can be traced by studying the RabbitMQ connection logs.
SQL injection attacks: These attacks involve injecting malicious SQL code into the RabbitMQ queries. Such attacks can be traced by studying the RabbitMQ exchange and queue logs.
Cross-site scripting (XSS) attacks: These attacks involve injecting malicious scripts into web pages served by RabbitMQ. Such attacks can be traced by studying the RabbitMQ channel logs.
Zero-day attacks: These attacks exploit unknown vulnerabilities in the RabbitMQ software. Such attacks can be traced by studying the RabbitMQ server logs.
Privilege escalation attacks: These attacks involve elevating user privileges to gain unauthorized access to the RabbitMQ cluster. Such attacks can be traced by studying the RabbitMQ connection and exchange logs.
Malware attacks: These attacks involve deploying malware in the RabbitMQ cluster. Such attacks can be traced by studying the RabbitMQ server logs.
Session hijacking attacks: These attacks involve stealing the session ID of an authenticated user to gain unauthorized access to the RabbitMQ cluster. Such attacks can be traced by studying the RabbitMQ connection logs.
Data theft attacks: These attacks involve stealing sensitive data stored in the RabbitMQ queues. Such attacks can be traced by studying the RabbitMQ exchange and queue logs.

Fail2ban

Fail2ban is an open-source software that provides intrusion prevention by scanning log files and detecting suspicious activity. It can be used to protect various services running on a server, such as SSH, Apache, Nginx, and others. Fail2ban works by monitoring log files for patterns that match known malicious behaviour, such as repeated failed login attempts or other types of suspicious activity. When a pattern is detected, it can take action to block the offending IP address or perform other actions, such as sending notifications or executing custom scripts. Fail2ban is a useful tool for enhancing server security and reducing the risk of successful cyber attacks.

It can collect logs related to failed login attempts, login timeouts, and other events related to authentication and access control. :

Auth logs: These logs track authentication attempts on a system, such as login attempts, failed authentication attempts, etc. Example: /var/log/auth.log
Syslog logs: These logs provide a centralized location for all system logs, including kernel and application logs. Example: /var/log/syslog
Apache logs: These logs record web server activity, such as requests, errors, and access attempts. Example: /var/log/apache2/access.log
Nginx logs: These logs record web server activity, such as requests, errors, and access attempts. Example: /var/log/nginx/access.log
SSH logs: These logs track SSH connections and authentication attempts. Example: /var/log/auth.log
Mail logs: These logs track email activity, such as sending, receiving, and delivery failures. Example: /var/log/mail.log
FTP logs: These logs track file transfer activity over FTP. Example: /var/log/vsftpd.log
DNS logs: These logs track DNS requests and responses. Example: /var/log/syslog
MySQL logs: These logs track MySQL database activity, such as connections, queries, and errors. Example: /var/log/mysql/error.log
Postfix logs: These logs track mail server activity for the Postfix mail transfer agent. Example: /var/log/mail.log

Here are some common cyber attacks that can be traced by studying Fail2ban logs:

Brute force attacks on SSH, FTP or web server login attempts.
Web application attacks such as SQL injection, cross-site scripting (XSS), and directory traversal.
Port scanning attempts to discover open ports and vulnerable services.
Spamming attempts by tracking email activity.
DDoS attacks targeting web or mail servers.
Exploit attempts against known vulnerabilities in web applications or services.
Credential stuffing attacks are where attackers use stolen credentials to gain access to a system.
Remote code execution attempts to execute arbitrary code on a system.
Shellshock attacks against vulnerable Bash commands.
Malware infection attempts by tracking unusual activity in system logs.

Custom logs

Custom logs are log messages generated by a software application or service that have been specifically configured by the system administrator or developer. Custom logs allow the capture of application-specific data that can be used for troubleshooting, performance monitoring, and security analysis.

Here are some examples of custom logs:

User activity logs: These logs record user actions within an application, such as login attempts, file downloads, or form submissions. This information can be used to identify suspicious behavior or to analyze application usage patterns.
Application error logs: These logs capture error messages and stack traces generated by an application when it encounters a problem. This information can be used to diagnose and fix bugs in the application.
Performance logs: These logs track performance metrics such as response time, throughput, and resource utilization. This information can be used to identify performance bottlenecks and optimize application performance.
Audit logs: These logs capture data related to compliance and regulatory requirements, such as user access, data modification, and system events.
Security logs: These logs capture security-related events, such as failed login attempts, access control changes, or network activity. This information can be used to detect and respond to security threats.
Custom business logs: These logs capture data specific to a particular business application or process. For example, an e-commerce website might log customer purchase history or shipping data.

Custom logs can be stored in various formats, such as plain text, JSON, or CSV, and can be analyzed using log analysis tools to extract insights and identify issues.

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