Machine Learning-Based Autonomous Physical Security Defences

DOI

10.2991/978-94-6463-471-6_118How to use a DOI?

Keywords

Internet-of-Battlefield Things (IoBT); Machine Learning Algorithms; Stealthier attacks; Intelligent anti-tamper; Physical attacks

Abstract

Nearly 50 billion linked devices by 2025 will make physical entry to the target system much easier for attackers. The proliferation of embedded devices in mission-critical infrastructure and industrial control systems, as well as the existence of the Internet of Battlefield Things (IoBT), heighten this risk. Existing anti-tamper designs have limited efficacy in preventing specific types of attacks and rely on predetermined responses to detect manipulation, which can undermine system reliability. More covert attacks are now feasible thanks to new physical inspection technology. Therefore, there is an immediate need for improved defences that can endure the anticipated rise in hostile capabilities for a considerable amount of time. If we want to take physical security to the next level, this study suggests building a smart anti-tamper with machine learning algorithms. It employs a number of analytical frameworks, one of which can distinguish between normal functioning, known attack vectors, and unusual behaviour. To further aid in the reduction of false alarms and enhancement of operating time, the system has a tiered reaction mechanism as well as a recovery strategy.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Subba Rao Polamurı
AU  - Knvpsb Ramesh
AU  - K. D. Srıhıtha
AU  - M. Srıdevı
AU  - M. Sangeetha
AU  - A. Yv. M. Gurudatta
PY  - 2024
DA  - 2024/07/30
TI  - Machine Learning-Based Autonomous Physical Security Defences
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1228
EP  - 1234
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_118
DO  - 10.2991/978-94-6463-471-6_118
ID  - Polamurı2024
ER  -