  An Intrusion Detection System (IDS) is a key cybersecurity tool for network
administrators as it identifies malicious traffic and cyberattacks. With the
recent successes of machine learning techniques such as deep learning, more and
more IDS are now using machine learning algorithms to detect attacks faster.
However, these systems lack robustness when facing previously unseen types of
attacks. With the increasing number of new attacks, especially against Internet
of Things devices, having a robust IDS able to spot unusual and new attacks
becomes necessary.
  This work explores the possibility of leveraging generative adversarial
models to improve the robustness of machine learning based IDS. More
specifically, we propose a new method named SIGMA, that leverages adversarial
examples to strengthen IDS against new types of attacks. Using Generative
Adversarial Networks (GAN) and metaheuristics, SIGMA %Our method consists in
generates adversarial examples, iteratively, and uses it to retrain a machine
learning-based IDS, until a convergence of the detection rate (i.e. until the
detection system is not improving anymore). A round of improvement consists of
a generative phase, in which we use GANs and metaheuristics to generate
instances ; an evaluation phase in which we calculate the detection rate of
those newly generated attacks ; and a training phase, in which we train the IDS
with those attacks. We have evaluated the SIGMA method for four standard
machine learning classification algorithms acting as IDS, with a combination of
GAN and a hybrid local-search and genetic algorithm, to generate new datasets
of attacks. Our results show that SIGMA can successfully generate adversarial
attacks against different machine learning based IDS. Also, using SIGMA, we can
improve the performance of an IDS to up to 100\% after as little as two rounds
of improvement.
