Walling up Backdoors in Intrusion Detection Systems
This addresses security vulnerabilities in intrusion detection systems, which is critical for network security, but it is incremental as it builds on existing defense methods.
The paper tackled the problem of backdoor attacks in intrusion detection systems, showing that common defense mechanisms fail, and proposed pruning-based approaches that effectively remove backdoors for decision trees and random forests on network security datasets.
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.