Lifelong Learning Intrusion Detection System

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Solution Overview

Problem

Existing Intrusion Detection Systems (IDS) face limitations in detecting both known and unknown network attacks due to the lack of continuous learning and high false positive rates, especially in anomaly-based detection methods, which require frequent updates and are not resilient to adversarial attacks.

Innovation Solution

A hybrid IDS architecture that integrates supervised and unsupervised machine learning with curiosity-based learning, utilizing a honeypot for additional insights and a rule-based decision framework for continuous updates, enabling the detection of diverse attacks and recognizing unknown/zero-day attacks through lifelong learning and cross-layer feature extraction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If anomaly-based detection is used to detect unknown attacks, then detection capability against zero-day attacks is improved, but false positive rate increases

Engineering Contradiction:

Improvedetection capability against zero-day attacksVSAvoidfalse positive rate

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent combines multiple detection methods (signature-based, anomaly-based, and hybrid approaches) into a unified IDS architecture that leverages the strengths of each method while mitigating their individual weaknesses, thereby reducing false positives while maintaining zero-day attack detection capability

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system implements continuous feedback mechanisms where detection results are fed back to update the detection models and signatures in real-time, allowing the system to learn from false positives and improve its accuracy while maintaining adaptability to new threats

Inventive Principle:

Principle #23Feedback

2Measurement precision

If signature-based detection is used to detect known attacks, then detection accuracy for known attacks is improved, but the system requires frequent updates to detect new attacks

Engineering Contradiction:

Improvedetection accuracy for known attacksVSAvoidability to detect new attacks without frequent updates

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary anomaly detection and behavioral analysis before final signature-based classification, allowing it to identify potential new threats early and prepare detection signatures in advance, reducing the need for frequent manual updates

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The IDS implements self-learning capabilities where the system automatically generates new detection signatures and updates its knowledge base based on analyzed attack patterns, enabling it to detect new attacks without requiring frequent external updates

Inventive Principle:

Principle #25Self-service

3Measurement precision

If traditional machine learning techniques are used for intrusion detection, then detection accuracy is improved, but the system lacks continuous learning capability to adapt to changing environments

Engineering Contradiction:

Improvedetection accuracyVSAvoidcontinuous learning capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic, evolving detection models that continuously adapt to changing network environments and attack patterns through online learning and incremental training, transforming static ML models into dynamic systems that improve over time

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system maintains continuous learning operations by constantly processing incoming traffic data to update detection models, ensuring the detection accuracy improves continuously without interruption or retraining cycles

Inventive Principle:

Principle #20Continuity of useful action

4Measurement precision

If existing IDS approaches are used, then detection performance is improved with large training data sets, but the system fails to perform well when training data is limited

Engineering Contradiction:

Improvedetection performanceVSAvoidtraining data set size requirement

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system applies partial training with selective, high-quality samples rather than requiring exhaustive large-scale training data, using techniques like active learning and sample prioritization to achieve effective detection performance with limited data

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12200014B2Lifelong learning based intelligent, diverse, agile, and robust system for network attack detection

Publication Date: 2025.01.14 PURDUE RES FOUND

AI summary

A lifelong learning intrusion detection system and methods are provided. The system may capture network data directed to a host node. The host node may include a honeypot. The honeypot may emulate operation of a physical or virtual device to attract malicious activity. The system may classify, based on a supervised machine learning model, the network data as being not malicious or not malicious. The system may classify, based on an unsupervised machine learning model, the network data as being anomalous or not anomalous. The system may alter operation of the honeypot to induce malicious activity. The system may determine, after operation of the honeypot is altered, the honeypot is accessed. The system may retrain the supervised machine learning model and/or unsupervised machine learning model based the network data.