4 research outputs found
Walling up Backdoors in Intrusion Detection Systems
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