Model Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning

Intrusion Detection Systems (IDS) have a long history as an effective network defensive mechanism. The systems alert defenders of suspicious and / or malicious behavior detected on the network. With technological advances in AI over the past decade, machine learning (ML) has been assisting IDS to improve accuracy, perform better analysis, and discover variations of existing or new attacks. However, applications of ML algorithms have some reported weaknesses and in this research, we demonstrate how one of such weaknesses can be exploited against the workings of the IDS. The work presented in this paper is twofold: (1) we develop a ML approach for intrusion detection using Multilayer Perceptron (MLP) network and demonstrate the effectiveness of our model with two different network-based IDS datasets; and (2) we perform a model evasion attack against the built MLP network for IDS using an adversarial machine learning technique known as the Jacobian-based Saliency Map Attack (JSMA) method. Our experimental results show that the model evasion attack is capable of significantly reducing the accuracy of the IDS, i.e., detecting malicious traffic as benign. Our findings support that neural network-based IDS is susceptible to model evasion attack, and attackers can essentially use this technique to evade intrusion detection systems effectively.