Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things

TL;DR

This paper introduces a two-stage IoT intrusion detection framework utilizing GAN-based adversarial training and deep learning to enhance security and robustness against adversarial attacks, achieving high detection accuracy.

Contribution

The paper presents a novel two-stage detection framework combining GAN adversarial training with deep learning for improved IoT security.

Findings

Detection accuracy of 96% achieved.

Robustness against adversarial attacks demonstrated.

Effective in identifying intrusions and adversarial examples.

Abstract

While the benefits of 6G-enabled Internet of Things (IoT) are numerous, providing high-speed, low-latency communication that brings new opportunities for innovation and forms the foundation for continued growth in the IoT industry, it is also important to consider the security challenges and risks associated with the technology. In this paper, we propose a two-stage intrusion detection framework for securing IoTs, which is based on two detectors. In the first stage, we propose an adversarial training approach using generative adversarial networks (GAN) to help the first detector train on robust features by supplying it with adversarial examples as validation sets. Consequently, the classifier would perform very well against adversarial attacks. Then, we propose a deep learning (DL) model for the second detector to identify intrusions. We evaluated the proposed approach's efficiency in terms of detection accuracy and robustness against adversarial attacks. Experiment results with a new cyber security dataset demonstrate the effectiveness of the proposed methodology in detecting both intrusions and persistent adversarial examples with a weighted avg of 96%, 95%, 95%, and 95% for precision, recall, f1-score, and accuracy, respectively.