Modern network intrusion detection systems rely on machine learning techniques to detect traffic anomalies and thus intruders. However, the ability to learn the network behaviour in real-time comes at a cost: malicious software can interfere with the learning process, and teach the intrusion detection system to accept dangerous traffic.
The recently published article presents an intrusion detection system (IDS) that is able to detect common network attacks including but not limited to, denial-of-service, bot nets, intrusions, and network scans.
With the help of the proposed example IDS, it is shown to what extent the training attack, and more sophisticated variants of it, have an impact on machine-learning based detection schemes. The analysis is then used to design an intrusion detection system that is resilient to such kind of attacks.