Webserver access logs are a valuable source of information about the traffic that your webserver is receiving. They can be used to identify potential threats, such as Denial of Service (DoS) attacks, and take appropriate action to mitigate them. In this article, we will discuss how to identify a DoS attack using webserver access logs.
What is a DoS Attack?
A Denial of Service (DoS) attack is a cyber-attack in which an attacker attempts to make a machine or network resource unavailable to genuine users by overwhelming it with a flood of illegitimate requests. DoS attacks can be launched against web servers, email servers, and other network services.
Use of Footprinting in DoS Attacks
Footprinting is the process of gathering information about a target system or network to identify vulnerabilities that can be exploited in an attack. Attackers use footprinting techniques to gather information about the target system, such as IP addresses, domain names, and network configurations, to plan and execute DoS attacks.
Footprinting Popular CMS Systems
Footprinting popular Content Management Systems (CMS) like WordPress, Joomla, and Drupal involves gathering information about the CMS version, plugins, themes, and configurations. Attackers use various tools and techniques to perform footprinting, such as:
- Online Tools: Websites like BuiltWith and Wappalyzer can identify the CMS and its components by analyzing the website's public information.
- Manual Inspection: Viewing the page source and looking for specific meta tags, comments, or file paths that indicate the CMS and its version.
- Automated Scanners: Tools like WPScan for WordPress can scan for vulnerabilities, enumerate plugins, and identify the CMS version.
- Directory Brute Forcing: Using tools like DirBuster to find hidden directories and files that reveal information about the CMS.
By gathering this information, attackers can identify potential vulnerabilities and plan their DoS attacks more effectively. It is crucial for website administrators to regularly update their CMS, plugins, and themes to mitigate the risks associated with footprinting.
But if you haven't even deployed a CMS like WordPress and something keeps trying to access files like wp-admin, it is pretty clear that your application is being profiled.
Threat Identification by Detecting Footprinting
It is possible for a threat identification system to prevent a DoS attack by switching to high aleart mode when it can find evidence of footprinting in webserver access logs.
Use of 404 Errors in DoS Attacks
One way to bring down a public facing website is to send it a flood of requests for non-existent pages. This will cause the webserver to return a 404 error for each request, consuming server resources and potentially making the website unavailable to genuine users. Attackers can use automated tools to generate a large number of requests for non-existent pages, causing a DoS condition. A threat identificatin system can detect a DoS attack by monitoring the number of 404 errors in the webserver access logs. If the number of 404 errors exceeds a certain threshold within a specified time frame, it may indicate a DoS attack in progress. If a network host keeps trying to access files which you don't have in your system, it is safe to block such IP address for some time.
Use of Slowloris Attack in DoS Attacks
Slowloris is a type of DoS attack that targets web servers by keeping many connections to the target web server open and hold them open as long as possible. It accomplishes this by opening connections to the target web server and sending a partial request. Periodically, it will send subsequent HTTP headers, adding to the request. The target server keeps the connection open, waiting for the request to be completed. However, the request is never completed.
How to Use IP Request IP Address to Prevent DoS Attacks
Every machine on the internet has got an IP address and if an IP address keeps causing 404 errors on your web-server, it is safe to block such IP address for some time. A threat identification system can send an alert to a web-server, firewall, or endpoint protection system about malicious IP addresses. It is the job of the threat protection mechanism to take preventive actions such as blocking or blacklisting ip addresses.
Use of Proxy to Avoid Detection
Attackers can use proxy servers to hide their real IP addresses and avoid detection by threat identification systems. By routing their traffic through multiple proxy servers, attackers can obfuscate their origin and make it difficult for defenders to identify and block them. It is essential for threat identification systems to monitor and analyze traffic patterns to detect and block malicious traffic, even if it is coming from proxy servers.
Use of Agent Information for Threat Detection
The User-Agent header in HTTP requests contains information about the client application, operating system, and device used to access the web server. Attackers can modify the User-Agent header to impersonate legitimate users or evade detection by threat identification systems. By analyzing the User-Agent header in webserver access logs, threat identification systems can identify suspicious or malicious traffic and take appropriate action to mitigate the threat. It is just another string which an intelligent attacker can manipulate to avoid detection. But it is still a valuable source of information for threat identification systems.
Using Wrong Agent Information for Threat Detection
If a user agent presents incoherent agent information to your web server, your fire alarms or danger detectors can identify such hosts and block any repeat offenders. One such example would be the pairing of Safari browser and Android phones. Or maybe the latest Chrome browser running on a Windows XP machine.
Tracking Request Headers Fields Size Limit
If you are using a web server that supports tracking request headers fields size, you can use the request headers size violation to detect DoS attacks. If the request headers size exceeds a certain limit, the web server will return a response with a 413 error code. This is a common practice in web servers that support tracking request headers fields size.
Using Machine Learning and AI for Threat Identification
No cybersecurity guide would be complete without mentioning machine learning and AI. Machine learning and AI can be used to analyze webserver access logs to identify patterns and anomalies that may indicate a potential threat. By training machine learning models on historical log data, these systems can learn to recognize normal traffic patterns and detect deviations that may signify a DoS attack or other malicious activity. Some ways machine learning and AI can be used for threat identification include:
- Anomaly Detection: Machine learning algorithms can be used to detect unusual patterns in webserver access logs that may indicate a potential threat. For example, a sudden spike in 404 errors or an unusual number of requests from a single IP address can be flagged as suspicious.
- Clustering: Clustering algorithms can group similar log entries together, making it easier to identify patterns and trends. For example, clustering can help identify groups of IP addresses that are behaving similarly, which may indicate a coordinated attack.
- Classification: Classification algorithms can be used to categorize log entries into different types of traffic, such as legitimate user traffic, bot traffic, or potential DoS attack traffic. This can help prioritize which log entries need further investigation.
- Predictive Analysis: Machine learning models can be trained to predict future threats based on historical log data. For example, if certain patterns of behavior are often followed by a DoS attack, the system can alert administrators to take preventive measures.
- Natural Language Processing (NLP): NLP techniques can be used to analyze the content of log entries, such as User-Agent strings, to identify suspicious or malicious behavior. For example, NLP can help detect if a User-Agent string is being manipulated to evade detection.
Popular Threat Identification Systems Using Log Analysis
Here are some of the most popular threat identification systems that utilize log analysis:
- Splunk: A powerful platform for searching, monitoring, and analyzing machine-generated data, including web server logs, to identify potential threats.
- ELK Stack (Elasticsearch, Logstash, Kibana): A popular open-source stack for log management and analysis, which can be used to detect and respond to security threats.
- Graylog: An open-source log management tool that provides real-time analysis and monitoring of log data for threat detection.
- Sumo Logic: A cloud-based log management and analytics service that helps identify security threats through log analysis.
- LogRhythm: A security information and event management (SIEM) platform that uses log data to detect, respond to, and mitigate security threats.
- QRadar: IBM's SIEM solution that analyzes log data to identify and respond to security incidents.
- AlienVault OSSIM: An open-source SIEM platform that combines log management, threat detection, and incident response capabilities.
- ArcSight: A SIEM solution by Micro Focus that uses log data to detect and respond to security threats.
Conclusion
Identifying and mitigating DoS attacks using webserver access logs is a crucial aspect of maintaining the security and availability of web services. By understanding the various techniques used by attackers, such as footprinting, exploiting 404 errors, and using proxy servers, administrators can implement effective threat identification systems. Leveraging machine learning and AI can further enhance the ability to detect and respond to potential threats in real-time. Regularly updating CMS systems, monitoring traffic patterns, and analyzing log data are essential practices for preventing DoS attacks and ensuring the resilience of web infrastructure.