showing performance over a wide range of possible systems.

Network Intrusion Detection Systems (NIDS) monitor network traffic to detect abnormal activities, such as attacks against hosts or servers. Machine learning algorithms offer the benefit of detecting novel differences in network traffic through training on normal and attack traffic. The traditional approach to designing a NIDS relies on an expert human analyst codifying rules that define normal behavior and intrusions. Due to the frequent failures of this approach to detect novel intrusions and the desire to lower the analyst’s workload, machine learning models are incorporated into NIDS with the goal of automating the process and supplementing the human effort.

On the other hand, the adoption of machine learning algorithms in the network intrusion detection setting has raised a major security issue, namely the introduction of adversarial machine learning. In this work, we study adversarial machine learning through the lens of NIDS to show the sensitivity of machine learning models when faced with adversarial attacks generated by perturbation techniques based upon evolutionary computation, deep learning, and Monte Carlo methods.

In precise terms, we use three different perturbation methods to generate adversarial examples: particle swarm optimization (PSO), a genetic algorithm (GA), and a 