Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey

Authors

Jaskaran Singh, Graphic Era Deemed to be University
Mohammad Wazid, Graphic Era Deemed to be University
Ashok Kumar Das, International Institute of Information Technology, Hyderabad
Vinay Chamola, Birla Institute of Technology and Science, Pilani
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

Computer Communications

Abstract

The cyber physical systems integrate the sensing, computation, control and networking processes into physical objects and infrastructure, which are connected through the Internet to execute a common task. Cyber physical systems can be applied in various applications, like healthcare, transportation, industrial production, environment and sustainability, and security and surveillance. However, the tight coupling of cyber systems with physical systems introduce challenges in addressing stability, security, efficiency and reliability. The machine learning (ML) security is the inclusion of cyber security mechanism to provide protection to the machine learning models against various cyber attacks. The ML models work through the traditional training and testing approaches. However, execution of such kind of approaches may not function effectively in case if a system is connected to the Internet. As online hackers can exploit deployed security mechanisms and poison the data. This data is then taken as the input by the ML models. In this article, we provide the details of various machine learning security attacks in cyber physical systems. We then discuss some defense mechanisms to protect against these attacks. We also present a threat model of ML security mechanisms deployed in cyber systems. Furthermore, we discuss various issues and challenges of ML security mechanisms deployed in cyber systems. Finally, we provide a detailed comparative study on performance of the ML models under the influence of various ML attacks in cyber physical systems.

First Page

316

Last Page

331

DOI

10.1016/j.comcom.2022.06.012

Publication Date

8-1-2022

Keywords

Authentication, Cyber physical systems (CPS), Intrusion detection, Machine learning (ML) security, Privacy and security

Comments

IR Deposit conditions:

OA version (pathway b) Accepted version

24 months embargo

License: CC BY-NC-ND

Recommended Citation

J. Sing, M. Wazid, A.K. Das, V. Chamola and M. Guizani, "Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey", in Computer Communications, vol. 192 (1), pp. 316-331, Aug 2022, doi: 10.1016/j.comcom.2022.06.012