Intrusion Prevention System Using ML for Real-Time Traffic Inspection
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Solution Overview
Problem
Intrusion prevention systems face challenges in real-time inspection of high-volume network traffic, making it impractical to employ machine learning techniques due to latency and performance requirements.
Innovation Solution
A machine learning model is used to classify network traffic by identifying features in a fixed-length data stream following an anchor pattern, allowing for real-time inspection and application of network policies, such as blocking malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to inspect network traffic, then detection accuracy is improved, but processing speed and real-time performance deteriorate
Solution Approach 1:
The patent segments the network traffic inspection process into multiple stages: initial pattern matching to identify potential threats, followed by machine learning analysis only on suspicious segments. This segmentation allows the system to maintain high processing speed for normal traffic while applying accurate ML detection only where needed, thus resolving the contradiction between detection accuracy and processing speed.
Solution Approach 2:
The system applies machine learning techniques partially - not to all network traffic, but only to traffic that passes initial filtering and shows suspicious characteristics. This partial application of ML maintains real-time performance for the majority of traffic while achieving high detection accuracy for potential threats, effectively balancing the contradiction between speed and accuracy.
2Reliability
If machine learning models are applied to inspect high-volume network traffic, then detection capability is improved, but system latency increases
Solution Approach 1:
The patent implements preliminary pattern matching and filtering actions before the machine learning analysis stage. By pre-processing traffic and identifying only suspicious patterns that warrant ML analysis, the system reduces the volume of traffic requiring intensive processing, thereby maintaining low latency while preserving detection capability for actual threats.
Solution Approach 2:
The system introduces an intermediary layer between raw network traffic and the machine learning model. This intermediary performs initial filtering, feature extraction, and candidate selection, preparing only relevant data for ML analysis. This intermediary mechanism reduces the computational burden on the ML model, maintaining real-time performance while preserving detection capability.
3Measurement precision
If comprehensive machine learning analysis is performed on network traffic, then intrusion detection accuracy is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent applies different quality levels of analysis to different portions of network traffic. Normal traffic receives minimal processing with high speed, while suspicious traffic identified through pattern matching receives comprehensive machine learning analysis with high accuracy. This local differentiation of analysis quality allows the system to maintain overall real-time processing capability while achieving high intrusion detection accuracy where it matters most.
Data Source
AI summary
An intrusion prevention system includes a machine learning model for inspecting network traffic. The intrusion prevention system receives and scans the network traffic for data that match an anchor pattern. A data stream that follows the data that match the anchor pattern is extracted from the network traffic. Model features of the machine learning model are identified in the data stream. The intrusion prevention system classifies the network traffic based at least on model coefficients of the machine learning model that are identified in the data stream. The intrusion prevention system apples a network policy on the network traffic (e.g., block the network traffic) when the network traffic is classified as malicious.