DDoS Detection via Reinforcement Learning and Dynamic Attack Trees
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Solution Overview
Problem
Current methods for intrusion detection and prevention in distributed computing systems are inadequate in handling dynamic environments, as they rely on static architectures and require supervised training with known attack types, leading to high false-positive rates and inability to detect previously unknown attacks effectively.
Innovation Solution
The system employs local data collection agents and an information layer agent to detect resource-related events, using reinforcement learning to dynamically modify attack tree models and responses, and applies clustering analysis to classify and mitigate both known and unknown attacks through a knowledge base and overlay network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static architecture and supervised training with known attack types are used for intrusion detection, then the system can detect known attacks, but it cannot detect previously unknown attacks and has high false-positive rates
Solution Approach 1:
The patent transforms the static intrusion detection architecture into a dynamic system using reinforcement learning. The detection system continuously adapts its parameters and decision boundaries based on real-time feedback from the environment, enabling it to detect both known and unknown attack types without requiring supervised training data for each specific attack variant.
Solution Approach 2:
The system employs autonomous reinforcement learning agents that self-improve their detection capabilities through continuous interaction with the network environment. The agents learn optimal detection strategies autonomously without human intervention or pre-labeled training data, allowing the system to adapt to new attack types as they emerge.
2Adaptability or versatility
If reinforcement learning is used to dynamically modify attack tree models, then the system can adapt to dynamic environments and detect unknown attacks, but the system complexity increases
Solution Approach 1:
The patent divides the intrusion detection system into multiple independent reinforcement learning agents, each responsible for specific aspects of attack detection. This segmentation allows the complex detection task to be distributed across multiple simpler agents, reducing the complexity burden on any single component while maintaining overall system adaptability.
Solution Approach 2:
The reinforcement learning framework serves multiple functions simultaneously: it detects known attacks, identifies unknown attack patterns, dynamically updates attack trees, and adapts to changing network conditions. This multi-functionality reduces the need for separate specialized systems, thereby managing complexity while enhancing adaptability.
3Reliability
If clustering analysis is applied to classify attacks through a knowledge base, then the system can reduce false positives and improve response efficiency, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes and clusters attack patterns during idle periods or using historical data, building a structured knowledge base of attack characteristics beforehand. When actual attacks occur, the system quickly matches observed behavior against this pre-built knowledge base, significantly reducing real-time processing requirements and false positives.
Solution Approach 2:
The clustering analysis is applied selectively to suspicious patterns rather than all network traffic. The reinforcement learning agents identify and focus computational resources only on potentially malicious activities that require detailed classification, leaving normal traffic to pass through with minimal processing, thus reducing overall processing time while maintaining detection accuracy.
Data Source
AI summary
Systems and methods for discovery and classification of denial of service attacks in a distributed computing system may employ local agents on nodes thereof to detect resource-related events. An information later agent may determine if events indicate attacks, perform clustering analysis to determine if they represent known or unknown attack patterns, classify the attacks, and initiate appropriate responses to prevent and/or mitigate the attack, including sending warnings and/or modifying resource pool(s). The information layer agent may consult a knowledge base comprising information associated with known attack patterns, including state-action mappings. An attack tree model and an overlay network (over which detection and/or response messages may be sent) may be constructed for the distributed system. They may be dynamically modified in response to changes in system configuration, state, and/or workload. Reinforcement learning may be applied to the tuning of attack detection and classification techniques and to the identification of appropriate responses.