Walling up backdoors in intrusion detection systems

摘要

Intrusion detection systems (IDSs) detect anomalies in a network. However, backdoors instated for profit, political gain, and/or other reasons can be hard to detect and may introduce security vulnerabilities. IDSs are generally built using multilayer perceptrons (MLPs). Much research is needed to detect backdoors in such a system. The authors' research is based on two methods: (1) An analysis of backdoors using visual tools and techniques; and (2) An analysis of techniques like pruning and fine-tuning. The authors theorize that, while such methods have been discussed for convolutional neural networks (CNNs), similar research is inadequate in detecting backdoors in traditional MLPs, decision trees (DTs), and random forests (RFs).