Hybrid Intrusion Detection System for Cyber Threat Intelligence
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Solution Overview
Problem
Current intrusion detection and prevention systems (IDS/IPS) generate excessive false alarms, require costly and time-consuming rule updates, and lack real-time protection, with no commercially available hybrid packet and flow-based approaches, limiting their effectiveness in detecting and preventing cyber threats.
Innovation Solution
A hybrid IDS/IPS apparatus and method integrating flow-based and packet-based machine learning techniques, featuring a network interface, feature extraction and selection models, intrusion detection modules, and an intrusion prevention module (IPM) for near real-time rule generation and optimization, enabling robust network threat detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional packet-based IDS/IPS is used for real-time intrusion detection, then real-time protection is provided, but certain cyber-attacks cannot be detected
Solution Approach 1:
The patent combines packet-based IDS/IPS for real-time detection with flow-based IDS/IPS for comprehensive attack detection. The hybrid architecture merges the speed advantages of packet-based systems with the detection capabilities of flow-based systems, allowing both real-time protection and accurate identification of complex cyber-attacks that require flow analysis
2Reliability
If flow-based IDS/IPS is used for comprehensive attack detection, then all types of attacks can be detected, but real-time protection cannot be provided
Solution Approach 1:
The system integrates flow-based detection capabilities with packet-based real-time processing. Flow-based analysis is used to detect complex attacks requiring comprehensive traffic patterns, while packet-based processing handles real-time threats, creating a balanced hybrid system that achieves both comprehensive detection and real-time protection
3Reliability
If more rules are added to IPS to improve detection coverage, then detection performance improves initially, but detection performance degrades over time due to rule complexity
Solution Approach 1:
The patent replaces traditional rule-based IPS mechanisms with machine learning models. Instead of manually adding and managing complex security rules, the system uses trained ML models that automatically learn attack patterns from data, eliminating the need for continuous rule updates and manual assessment while maintaining high detection coverage
Solution Approach 2:
The machine learning models perform self-training and self-optimization by automatically learning from new attack patterns and traffic data. The system self-adjusts its detection capabilities without requiring external rule updates or manual intervention, maintaining optimal performance over time without accumulating complexity
4Reliability
If IDS/IPS is deployed as network gateway for single network environment, then protection is provided for that specific network, but the system lacks adaptability to different network environments
Solution Approach 1:
The machine learning-based IDS/IPS is designed to be environment-agnostic, training models on diverse network traffic data that encompasses multiple network types and configurations. This allows the same system to be deployed across different network environments (enterprise, cloud, IoT, etc.) without requiring environment-specific customization, achieving universal applicability while maintaining effective protection
5Extent of automation
If machine learning models are used for intrusion detection, then intelligent detection and near real-time processing are achieved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual rule management and expert assessment with automated machine learning models. The ML models automatically perform feature extraction, pattern recognition, and threat classification, eliminating the need for security analysts to manually assess and update detection rules, thereby achieving intelligent automation while managing system complexity through algorithmic efficiency
Data Source
AI summary
An apparatus for intrusion detection and prevention of cyber threat intelligence is provided. The apparatus includes a feature extraction model, a feature selection model, a flow-based intrusion detection module, a packet-based intrusion detection module, and an IPM. The feature extraction model is configured to parse packets from network raw packet data into sessions as a network flow dataset. The feature selection model is configured to select flow-based features from the network flow dataset to generate network flow meta. The flow-based intrusion detection module is configured to generate flow-based labels. The packet-based intrusion detection module is configured to generate packet-based labels. The IPM is configured to perform intrusion analysis according to the flow-based labels and the packet-based labels so as to generate IPM rules for intrusion detection and prevention, thereby identifying and processing the network raw packet data.