Cluster-Based Anomaly Detection for DDoS Mitigation
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Solution Overview
Problem
Traditional methods for detecting distributed denial of service (DDoS) attacks in TCP/IP traffic rely on slope and ratio calculations, which struggle to dynamically generate policies to mitigate anomalies without affecting normal traffic, as the intent behind traffic patterns is often unknown.
Innovation Solution
A cluster-based method that captures data packets, groups them using a K-means algorithm, determines anomalies, and generates rules based on signatures to block malicious traffic, optimizing these rules using a genetic algorithm for policy enforcement points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional slope and ratio calculations are used to detect DDoS attacks, then anomaly detection capability is improved, but the ability to dynamically generate policies without affecting normal traffic deteriorates
Solution Approach 1:
The patent segments traffic analysis into cluster-based grouping, where traffic patterns are divided into distinct clusters representing different behaviors. This segmentation allows the system to identify anomalous clusters without creating overly broad policies that would affect normal traffic, resolving the contradiction between detection capability and policy adaptability
Solution Approach 2:
The system dynamically generates policies based on real-time cluster analysis rather than using static threshold-based rules. The policy generation adapts to changing traffic patterns by continuously updating cluster assignments and identifying anomalies, enabling dynamic policy creation that preserves normal traffic while blocking attacks
2Reliability
If dynamic policies are generated to mitigate anomalies, then the effectiveness of attack mitigation is improved, but the disruption to normal traffic flows increases
Solution Approach 1:
The patent applies local quality by generating targeted policies specific to each identified anomaly cluster rather than applying blanket blocking rules. Each policy is locally optimized to address specific malicious patterns while preserving normal traffic characteristics, thus maintaining mitigation effectiveness without disrupting legitimate flows
Solution Approach 2:
The system implements feedback mechanisms where policy effectiveness is continuously monitored and used to refine future policy generation. By analyzing the impact of generated policies on traffic flows, the system adjusts subsequent policy creation to maintain attack mitigation while minimizing disruption to normal traffic
Data Source
AI summary
Provided are methods and systems for cluster-based determination of signatures for detection of anomalous data traffic. An example method may include capturing, by a network module, data packets routed to a destination. The method may further include grouping, by at least one processor in communication with the network module, the data packets into clusters. The method may also include detecting, by the processor, an anomaly in the data packets and, in response to the detection, determining, by the processor and based on the clusters, one or more signatures associated with the data packets. The method may further include generating, by the processor and based on the signatures, one or more rules for allowing the data packets. The method may further include providing, by the processor, the one or more rules to a policy enforcement point associated with the destination.