Walling up Backdoors in Intrusion Detection Systems

Abstract

Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications. Several successful defense mechanisms have been recently proposed for Convolutional Neural Networks (CNNs), for example in the context of autonomous driving. We show that visualization approaches can aid in identifying a backdoor independent of the used classifier. Surprisingly, we find that common defense mechanisms fail utterly to remove backdoors in DL for Intrusion Detection Systems (IDSs). Finally, we devise pruning-based approaches to remove backdoors for Decision Trees (DTs) and Random Forests (RFs) and demonstrate their effectiveness for two different network security datasets.

References in corpus (6)

• Scikit-learn: Machine Learning in Python
• How transferable are features in deep neural networks?
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• Robust Decision Trees Against Adversarial Examples
• Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Cited by in corpus (4)

• The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
• Explainability and Adversarial Robustness for RNNs
• Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
• SparseIDS: Learning Packet Sampling with Reinforcement Learning