Document Type

Article

Publication Date

2023

DOI

10.1109/ACCESS.2023.3291599

Publication Title

IEEE Access

Volume

11

Pages

74000-74020

Abstract

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

Original Publication Citation

Hussain, S., Ahmad, M. B., Asif, M., Akram, W., Mahmood, K., Das, A. K., & Shetty, S. (2023). APT adversarial defence mechanism for industrial IoT enabled Cyber-Physical System. IEEE Access, 11, 74000-74020. https://doi.org/10.1109/ACCESS.2023.3291599

ORCID

0000-0002-8789-0610 (Shetty)

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