Network Intrusion Detection via Dynamic Threshold Adaptation
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Solution Overview
Problem
Current network intrusion detection systems are vulnerable to DoS and DDoS attacks, particularly due to rate-based filtering approaches that can be evaded by attackers learning and adjusting their traffic rates below predefined thresholds, leading to persistent attacks that evade detection.
Innovation Solution
The system employs a Deep Packet Field Extraction Engine (DPFEE) to determine the starting point of header fields in network packets using a header finder module and delimiter finder module, and utilizes Hardware Performance Counters (HPCs) to detect anomalies through clustering and support vector machines, dynamically adjusting thresholds to mitigate DDoS attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rate-based filtering approaches with static thresholds are used for DDoS detection, then the detection mechanism is simple to implement, but the system becomes vulnerable to threshold learning attacks where attackers can evade detection by adjusting their traffic rates below the threshold
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously monitoring network traffic patterns and adapting the threshold values in real-time. Instead of using fixed static thresholds that attackers can learn and exploit, the system dynamically modifies thresholds based on observed traffic characteristics, making it difficult for attackers to predict or evade detection. This dynamic adaptation resolves the contradiction by maintaining simple rate-based filtering while eliminating the reliability vulnerability through continuous adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and traffic patterns are continuously fed back into the threshold adjustment process. The monitoring component analyzes detected attacks and legitimate traffic patterns, then adjusts thresholds accordingly to prevent both false positives and threshold learning attacks. This feedback loop maintains implementation simplicity while significantly improving detection reliability by constantly optimizing the detection parameters based on actual system performance.
2Measurement precision
If deep packet inspection is performed to accurately identify attack patterns, then detection accuracy improves, but processing time increases and resource exhaustion occurs
Solution Approach 1:
The patent segments the packet inspection process into multiple hierarchical levels, starting with shallow inspection of packet headers and metadata, then progressively deepening inspection only for packets that exhibit suspicious characteristics. This segmented approach allows the system to maintain high detection accuracy for attacks while avoiding the time penalty of deep inspection on all packets. The segmentation resolves the contradiction by applying computational resources selectively rather than uniformly.
Solution Approach 2:
The system applies different inspection depths to different packets based on their characteristics. Packets showing signs of malicious behavior undergo deep packet inspection with full accuracy, while normal packets receive minimal inspection. This local quality approach ensures high detection accuracy where needed while maintaining fast processing for bulk traffic, thereby resolving the time-accuracy trade-off.
3Ease of operation
If static threshold values are used for rate-based filtering, then the detection system is easy to configure, but attackers can learn the threshold and craft malicious traffic below the threshold to persistently affect the victim
Solution Approach 1:
The patent transforms static threshold configuration into a dynamic system that automatically adapts to changing attack patterns. The system continuously learns from observed traffic and automatically adjusts thresholds without requiring manual reconfiguration. This maintains ease of operation during initial setup while providing continuous adaptability to new attack methods, resolving the contradiction between ease of configuration and adaptability.
Solution Approach 2:
The detection system performs self-adjustment of thresholds based on its own observations of traffic patterns and attack behaviors. Rather than requiring external reconfiguration for new attack types, the system autonomously adapts its detection parameters. This self-service capability maintains simple initial configuration while providing ongoing adaptability to evolving threats.
Data Source
AI summary
An exemplary system, method and computer-accessible medium for determining a starting point of a header field(s) in a network packet(s) can be provided, which can include, for example receiving the network(s) packet, determining a header location of the header field(s) in the network packet(s), determining a delimiter location of a delimiter(s) in the network packet(s), and determining the starting point of the header field(s) based on the header and delimiter locations. The header location can be determined using a header finder module. The delimiter location can be determined using a delimiter finder module. The header and delimiter locations can be determined using a plurality of comparators arranged into a plurality of sets.