DDOS Detection via Statistical Analysis of Web Server Logs
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Solution Overview
Problem
Current methods for detecting and mitigating distributed denial of service (DDOS) attacks in networks are inefficient, requiring extensive human intervention and analysis of network logs, which prolongs the time to resolve attacks.
Innovation Solution
A system that uses statistical analysis of web server logs to identify potential DDOS attacks by tracking source addresses and applying human behavior analysis (HBA) to differentiate between legitimate and malicious requests, automating the process and reducing the time to mitigate attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature or heuristic-based detection is used to analyze network traffic, then detection accuracy is improved, but the process requires extensive human intervention and analysis time increases
Solution Approach 1:
The detection process is segmented into multiple stages: initial statistical analysis of request frequencies from various source addresses, identification of anomalous patterns, and then focused human review only of suspicious cases. This segmentation reduces the volume of data requiring human analysis while maintaining detection accuracy.
Solution Approach 2:
Statistical analysis and pattern recognition are performed preliminarily before human intervention. The system pre-processes network traffic data, calculates request frequencies, identifies potential DDOS patterns, and prepares candidate cases for human review, thereby reducing the time required for human analysts to perform initial screening.
2Measurement precision
If extensive human intervention is used to analyze network logs, then detection accuracy is improved, but productivity decreases
Solution Approach 1:
Statistical analysis algorithms and automated pattern recognition systems serve as intermediaries between raw network traffic data and human analysts. These intermediaries perform initial filtering, scoring, and prioritization of suspicious traffic patterns, allowing human analysts to focus their expertise on the most critical cases rather than manually examining all traffic data.
Solution Approach 2:
Manual mechanical analysis of network logs by human analysts is partially replaced with automated statistical analysis systems that can rapidly process large volumes of traffic data, calculate frequencies, and identify patterns. This substitution maintains detection accuracy for obvious patterns while significantly increasing processing throughput.
3Productivity
If statistical analysis and automated processes are used, then productivity is improved, but detection precision may be reduced
Solution Approach 1:
The system incorporates feedback loops where automated statistical analysis results are reviewed and validated by human analysts. Detection outcomes are fed back into the system to refine statistical models and thresholds, improving both the speed and accuracy of automated detection over time. Human analysts can adjust parameters based on false positives or negatives identified during review.
Solution Approach 2:
The statistical analysis system allows dynamic adjustment of detection parameters such as threshold values, time windows, and confidence levels. These parameter changes enable the system to adapt to different attack patterns and traffic conditions, maintaining high detection accuracy while operating at automated speeds. Parameters can be tuned based on historical data and emerging threat patterns.
Data Source
AI summary
Embodiments can identify requests that may be tied to a DDOS attack. For example, the primary identifiers (e.g., a source address) of requests for a network resource (e.g., an entire website or a particular element of the website) can be tracked. In one embodiment, a statistical analysis of how often a particular source address (or other primary identifier) normally makes a request can be used to identify source addresses that make substantially more requests. A normal amount can correspond to an average number of request that a source address makes. According to some embodiments, a system can use statistical analysis methods on various request data in web server logs to identify potential attacks and send data concerned potential attacks to an HBA system for further analysis.