Detecting and Eliminating Neural Network Backdoors Through Active Paths with Application to Intrusion Detection

2026-03-11

AI Summary

This work addresses the threat of backdoor attacks in neural networks, which exhibit normal behavior on benign inputs but execute attacker-specified actions when triggered by a specific pattern, thereby remaining highly stealthy. To counter this, the paper introduces active path analysis—a novel approach to backdoor detection and removal—that offers both interpretability and practical utility. By identifying anomalous activation paths uniquely triggered by the backdoor pattern, the method effectively localizes and eliminates the embedded backdoor. Experimental evaluation on compromised intrusion detection models demonstrates that the proposed technique accurately identifies and successfully neutralizes backdoor triggers, confirming its effectiveness and robustness.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Active Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Machine learning backdoors have the property that the machine learning model should work as expected on normal inputs, but when the input contains a specific $\textit{trigger}$, it behaves as the attacker desires. Detecting such triggers has been proven to be extremely difficult. In this paper, we present a novel and explainable approach to detect and eliminate such backdoor triggers based on active paths found in neural networks. We present promising experimental evidence of our approach, which involves injecting backdoors into a machine learning model used for intrusion detection.

Problem

• neural network backdoors
• backdoor detection
• trigger identification
• intrusion detection
• machine learning security

Innovation

• backdoor detection
• active paths
• neural network security
• explainable AI
• intrusion detection