Foundation Projects

Adversarial Machine Learning Attacks in Wireless Networks

Griffith University

works in recent years, enabled by the advances in machine learning techniques and computational hardware resources. Driven by the various data characteristics of wireless communications (e.g. channel status, signal strength, noise power, traffic pattern), machine learning can be used to optimally tune the various wireless network parameters and hence optimize the network performance. However machine learning is also vulnerable to adversarial attacks. Malicious attackers can use the same data characteristics of wireless communications in order to design their attacks and tamper with the networks’ learning process or interfere with their network communications. These attacks can cause the wireless networks performance to degrade substantially. In this pilot project, we have investigated adversarial machine learning attacks in Wi-Fi wireless networks using the reinforcement learning model. We have implemented models of the adversarial machine learning attacks in the ns-3 network simulator and evaluated the impact of these attacks on the network throughput performance. It is essential to better understand these adversarial attacks to enable one to design effective countermeasures against them and therefore lead to the safe adoption of machine learning applications in wireless networks. 

The main activities conducted in this project include 

  • extensive literature survey to understand current works in applying reinforcement learning (RL) to Wi-Fi rate adaptation algorithms
  • design, implementation and evaluation of our own RL-based Wi-Fi rate-adaptation algorithm (named ReinRate) in the ns-3 network simulator
  • extensive literature survey to understand current works in adversarial machine learning attacks in wireless networks
  • design, implementation and evaluation of an adversarial machine learning attack model that can negatively impact the throughput performance of Wi-Fi network nodes in the ns-3 network simulator

The code base developed in this project (both the ReinRate and adversarial attack works) are available for download on GitHub:

These are publicly available to enable other researchers in the world to extend on our work and further the knowledge development on reinforcement learning models as applied to Wi-Fi wireless networks.

We have also written a paper on the ReinRate rate adaptation algorithm which has been accepted and will be presented in the 2024 IEEE Wireless Communications and Networking Conference (WCNC) in Dubai. The paper is titled "A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm" and will be available for download from IEEE Xplore digital library.