DDoS Detection via Behavioral Vector Analysis
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Solution Overview
Problem
Current technologies are ineffective in detecting and mitigating distributed denial-of-service (DDoS) attacks, particularly slow-rate attacks that resemble legitimate traffic and exploit protocol deficiencies, rendering traditional methods useless.
Innovation Solution
A system and method that generate vectors based on user characteristics, comparing them to a reference vector of averaged user distributions to determine if a service is under attack, utilizing a DDoS protection module to differentiate between legitimate and malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify DDoS attacks, then bandwidth-based attacks can be detected, but slow-rate attacks cannot be distinguished from legitimate traffic
Solution Approach 1:
The patent creates a universal detection system that analyzes user request characteristics across multiple protocols (HTTP, SSL/TLS, TCP) without being protocol-specific. The system extracts behavioral features such as request timing, header patterns, and connection characteristics that are applicable across different protocols, enabling detection of slow-rate attacks regardless of the specific protocol being used.
Solution Approach 2:
The system transforms attack detection from bandwidth-based metrics to behavioral parameter analysis. By monitoring parameters such as request interval, header composition, connection duration, and timing patterns, the system can detect slow-rate attacks that generate minimal bandwidth but exhibit abnormal behavioral parameters compared to legitimate users.
2Measurement precision
If protocol-specific analysis is used to detect attacks, then detection accuracy for known protocols improves, but the system becomes useless when protocols change or new versions are released
Solution Approach 1:
The patent creates a universal detection system that analyzes user request characteristics across multiple protocols (HTTP, SSL/TLS, TCP) without being protocol-specific. The system extracts behavioral features such as request timing, header patterns, and connection characteristics that are applicable across different protocols, enabling detection of slow-rate attacks regardless of the specific protocol being used.
Solution Approach 2:
The system dynamically adapts to different protocol versions and types by continuously learning normal user behavior patterns. Rather than relying on static protocol rules, the system adjusts its analysis based on observed behavioral characteristics, allowing it to remain effective when protocols evolve or new versions are released.
3Quantity of substance
If traditional mitigation methods are applied to slow-rate attacks, then bandwidth is preserved, but the attacks cannot be mitigated because they resemble legitimate traffic
Solution Approach 1:
The system transforms attack detection from bandwidth-based metrics to behavioral parameter analysis. By monitoring parameters such as request interval, header composition, connection duration, and timing patterns, the system can detect slow-rate attacks that generate minimal bandwidth but exhibit abnormal behavioral parameters compared to legitimate users.
Solution Approach 2:
The system implements continuous monitoring and feedback mechanisms that analyze user behavior patterns in real-time. When abnormal patterns are detected, the system can dynamically adjust mitigation strategies, blocking malicious traffic while preserving legitimate traffic flow, thus maintaining service availability without requiring bandwidth-based restrictions.
Data Source
AI summary
Disclosed are systems and methods for detecting distributed denial-of-service (DDoS) attack. An exemplary method includes receiving one or more requests from a first user for a service executing on a server, and generating a first vector associated with the first user comprised of a plurality of characteristics indicative of the first user accessing the service; calculating a comparison between the first vector and a reference vector, wherein the reference vector comprises an averaged distribution of characteristics for a plurality of users accessing the service, and determining that the service is under a denial-of-service attack based on the comparison between the first vector and the reference vector.