DDOS Detection via Modular Traffic Filtering and Feature Extraction
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Solution Overview
Problem
Current systems are ineffective in detecting distributed denial of service (DDOS) attacks in real-time due to their complexity and the difficulty in tracing the origin of such attacks, leading to potential prolonged exposure and significant economic losses for targeted computer systems.
Innovation Solution
A modular system comprising a preprocessing module to filter and condition traffic, a perception module to extract features, a detection module using machine learning algorithms like Hebbian learning and Adaptive Resonance Theory, and a mitigation module to respond to detected cyberattacks, enabling real-time detection and mitigation of DDOS attacks with low computing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection systems are used to monitor network traffic for DDOS attacks, then they can provide continuous monitoring, but they fail to detect attacks in real-time due to complexity and computational requirements
Solution Approach 1:
The system segments the detection process into distinct functional modules: preprocessing module for traffic filtering, perception module for feature extraction, detection module for anomaly identification, and mitigation module for response actions. This segmentation allows each module to specialize in specific tasks, improving detection accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediary components between traffic input and detection analysis, including preprocessing modules that filter and condition traffic data, and perception modules that extract relevant features. These intermediaries simplify the detection process by preparing data in advance, reducing the computational burden on the detection module while maintaining high detection accuracy.
2Measurement precision
If complex analysis algorithms are applied to detect DDOS attacks, then detection precision may improve, but processing time increases preventing real-time detection
Solution Approach 1:
The system performs preliminary actions by preprocessing traffic data before detection analysis. The preprocessing module filters and conditions traffic in advance, and the perception module extracts relevant features beforehand. This preliminary processing reduces the complexity of subsequent detection operations, enabling real-time detection without sacrificing precision.
Solution Approach 2:
The patent applies local quality by focusing detection resources on specific critical features rather than analyzing all traffic data uniformly. The perception module extracts only the most relevant features from traffic data, and the detection module concentrates analysis on these selected features, achieving high detection precision with reduced processing time.
3Reliability
If the system monitors all traffic data in detail, then detection accuracy improves, but computing power requirements increase beyond low-resource capabilities
Solution Approach 1:
The system extracts only the essential features from traffic data that are necessary for detecting DDOS attacks. The perception module identifies and extracts critical characteristics from traffic flows, discarding redundant information. This extraction approach maintains detection reliability while significantly reducing computing power requirements, enabling deployment on low-resource devices.
4Productivity
If the system responds immediately to detected attacks, then mitigation effectiveness improves, but false positives may increase causing unnecessary disruptions
Solution Approach 1:
The system implements feedback mechanisms where detection results are analyzed and validated before triggering mitigation actions. The detection module provides output signals that are processed to assess confidence levels and attack characteristics. This feedback approach allows the system to respond quickly to confirmed attacks while filtering out false positives, maintaining both response speed and reliability.
Data Source
AI summary
A system for analyzing internet traffic passing through an exposed computer device includes a preprocessing module for filtering the traffic so as to substantially isolate from the traffic features carrying data representative of a cyberattack, a perception module for extracting the data from the isolated features, a detection module for processing the extracted data to detect characteristics indicative of the cyberattack, and a mitigation module for generating responsive action if a cyberattack is detected.