Statistical Analysis for DDOS Attack Identification
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Solution Overview
Problem
Current methods for detecting and mitigating distributed denial of service (DDOS) attacks in networks require significant human intervention and analysis, leading to inefficiencies in identifying and resolving potential attacks.
Innovation Solution
A system that uses statistical analysis of web server logs to identify potential DDOS attacks by tracking request patterns and outliers, with further analysis by human behavior analysis (HBA) to determine malicious activity, thereby automating the process and reducing the time required 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 capability is improved, but the process requires high degree of human intervention and analysis
Solution Approach 1:
The system automatically analyzes network traffic patterns and identifies DDOS attacks without requiring human analysts to manually examine each traffic sample. The automated analysis engine processes traffic data, applies detection algorithms, and generates attack identification results independently, eliminating the need for human intervention in the detection process while maintaining high detection precision through statistical anomaly detection and pattern recognition
2Measurement precision
If manual analysis of network traffic is performed, then detection accuracy is improved, but the time required to identify and mitigate attacks increases
Solution Approach 1:
The system replaces manual mechanical analysis of network traffic with automated computational analysis. The automated analysis engine uses statistical methods, pattern recognition algorithms, and machine learning models to process traffic data at high speed, achieving both high detection accuracy and rapid response times. This substitution enables real-time or near-real-time attack identification and mitigation without the time constraints of human analysis
Solution Approach 2:
The system performs preliminary analysis of network traffic patterns continuously, even during normal operations, to establish baseline behavior and detect anomalies as they emerge. By maintaining continuous automated monitoring and pre-processing of traffic data, the system is prepared to rapidly identify and respond to DDOS attacks the moment they occur, significantly reducing the time from attack initiation to mitigation while maintaining high detection accuracy through ongoing pattern recognition
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.