Multi-Dimensional Anomaly Detection for Selective DDoS Traffic Isolation
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Solution Overview
Problem
Existing solutions for detecting distributed denial-of-service (DDoS) attacks in cloud environments rely on single-dimensional traffic analysis and static rules, leading to inaccurate and unreliable protection, which can disrupt legitimate traffic and services.
Innovation Solution
Implement a multi-dimensional anomaly detection system that monitors system load metrics and performs multi-dimensional analysis of traffic data using security modules like tenant, IP, and connection trackers, generating adaptive anomaly scores to identify and isolate malicious traffic sources while allowing legitimate traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-dimensional traffic analysis with static rules is used, then device complexity is reduced, but measurement precision and reliability of anomaly detection deteriorate
Solution Approach 1:
The patent transitions from single-dimensional traffic analysis to multi-dimensional analysis by incorporating multiple traffic metrics (request rate, connection state, byte rate, packet rate) and analyzing them across multiple dimensions simultaneously. This dimensional expansion enables more precise anomaly detection without requiring excessive system complexity.
Solution Approach 2:
The detection system is segmented into multiple independent analysis modules, each responsible for specific traffic dimensions. This segmentation allows the complex multi-dimensional analysis to be broken down into manageable components, maintaining system simplicity while achieving high detection precision through coordinated module operation.
2Ease of operation
If static rules are used for anomaly detection, then ease of operation is improved, but adaptability to different attack patterns deteriorates
Solution Approach 1:
The system implements dynamic anomaly detection by continuously monitoring multiple traffic dimensions and automatically adjusting detection parameters based on observed patterns. This dynamic approach replaces static rules with adaptive algorithms that automatically respond to different attack patterns, maintaining ease of operation while significantly improving adaptability.
Solution Approach 2:
The patent changes detection parameters dynamically based on traffic conditions and identified attack patterns. By adjusting thresholds, weights, and analysis focus according to real-time observations, the system achieves high adaptability to various DDOS attack types without requiring manual reconfiguration, thus maintaining operational simplicity.
3Reliability
If multi-dimensional analysis with multiple security modules is implemented, then measurement precision and reliability of detection are improved, but device complexity increases
Solution Approach 1:
The patent merges multiple security modules (traffic analysis, connection tracking, anomaly detection, rate limiting) into a unified integrated system. This consolidation achieves reliable multi-dimensional detection while managing complexity by coordinating modules through a centralized architecture that shares data and coordinates actions across all components.
Solution Approach 2:
The detection system is designed with universal components that perform multiple functions. For example, the traffic analysis module simultaneously monitors multiple dimensions (rate, connection state, byte/packet volume) and feeds information to multiple detection algorithms. This multi-functionality reduces overall system complexity while maintaining high reliability through comprehensive analysis.
4Object-affected harmful factors
If aggressive anomaly blocking is performed, then protection against attacks is improved, but harmful factors increase due to disruption of legitimate traffic
Solution Approach 1:
The system applies partial blocking actions rather than complete traffic cessation. By implementing rate limiting and selective filtering that blocks only the excessive malicious portion of traffic while allowing legitimate traffic to pass, the system achieves strong attack protection without generating harmful disruptions to normal services.
Solution Approach 2:
The patent applies different quality levels of filtering to different traffic streams. Legitimate traffic receives minimal intervention with high-quality pass-through, while suspicious traffic undergoes stricter inspection and selective blocking. This localized quality approach ensures strong protection against attacks while minimizing disruption to legitimate users based on their specific traffic characteristics.
Data Source
AI summary
Adaptive multi-dimensional anomaly detection is provided. System load metrics of a computing device are monitored. Multi-dimensional analysis of traffic data from a plurality of traffic sources is performed with security modules of an anomaly detector on the computing device. A traffic source is identified from the plurality of traffic sources based on the multi-dimensional analysis of traffic data from the plurality of traffic sources and associated historical traffic data. An action is performed on the traffic data from the identified traffic source, while the traffic data from the plurality of traffic sources other than the identified traffic source is allowed unaffected.