Dynamic Network Protection via Adaptive Fuzzy Logic
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Solution Overview
Problem
Computer networks face challenges in detecting and mitigating malicious traffic, particularly stateless DoS and DDoS attacks, as existing systems rely on signature-based detection methods that are ineffective against evolving attack patterns and resource-intensive for stateful inspections.
Innovation Solution
A dynamic network security system employing adaptive fuzzy logic algorithms to analyze traffic patterns in real-time, using feedback control loops to optimize filtering and minimize false positives, and implementing stateless and stateful inspection layers to detect and filter various types of attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used, then known attack patterns can be detected, but the system is ineffective against evolving attack patterns and resource-intensive
Solution Approach 1:
The patent implements dynamic threshold adjustment where detection thresholds are not fixed but adapt based on learned normal traffic patterns. The system continuously updates its understanding of baseline traffic behavior and adjusts detection sensitivity accordingly, enabling it to detect evolving attacks without requiring manual signature updates.
Solution Approach 2:
The system performs self-learning by automatically analyzing traffic patterns and establishing baseline behavior without external intervention. It autonomously adapts to changing network conditions and attack patterns, reducing dependency on manual signature maintenance while improving detection of novel threats.
2Reliability
If stateful inspection is performed, then connection-based attacks can be detected, but resource consumption increases
Solution Approach 1:
The patent divides inspection into two distinct layers: stateless inspection for initial packet filtering and stateful inspection for connection-based attack detection. This segmentation allows the system to apply computationally intensive stateful inspection only when necessary, rather than to all traffic, thereby reducing overall resource consumption while maintaining detection effectiveness.
Solution Approach 2:
The system applies stateful inspection partially - only to traffic that requires connection state verification - rather than excessively inspecting all packets. This selective approach maintains adequate detection capability while minimizing unnecessary resource expenditure on traffic that can be handled by lighter inspection methods.
3Reliability
If filtering rules are made more aggressive, then malicious traffic is blocked more effectively, but false positives increase and legitimate traffic is impacted
Solution Approach 1:
The patent implements feedback mechanisms where detection results and traffic patterns are continuously monitored and used to adjust filtering thresholds. The system learns from false positives and genuine attacks, dynamically tuning its sensitivity to maintain effective blocking while minimizing false alarms, creating a self-optimizing detection system.
Data Source
AI summary
A method for protecting a network from an attack includes measuring a property of traffic entering the network, and analyzing the property using at least one fuzzy logic algorithm in order to detect the attack.