Analysis of Adversarial Attacks in Network Security

Solution Architecture
Fig. 1 Solution Architecture

Project Description:

The NGN laboratory is actively researching into the intersection of Network Security and Artificial Intelligence (AI) techniques such as Deep Learning. In this research project, we analyze the adversarial risks of applying deep learning methods to detect and mitigate security threats against IoT networks.

An adversarial attack on a deep learning model occurs when an adversarial example is fed as an input to the deep learning model. An adversarial example is an instance of the input in which some feature has been intentionally perturbed with the intention of confusing the deep learning model to produce a wrong prediction.

FNN and SNN comparison results
Fig. 2 Comparison of FNN and SNN architectures

We evaluate two types of deep learning models. A feed forward artificial neural network (FNN) and a self-normalizing neural network (SNN). In our experimental setup, we photo two network intrusion detection systems using each variant of the deep learning model. The intrusion detection systems are then used to analyze network attack traffic of the BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber.

[1.] Olakunle Ibitoye, M. Omair Shafiq and Ashraf Matrawy, "Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks", in Proc. of the IEEE Global Communications Conference (GLOBECOM 2019), (Accepted) Waikoloa, HI, USA, 9 - 13 December 2019.
  • Olakunle Ibitoye
  • Supervisors
  • Ashraf Matrawy
  • M. Omair Shafiq