Toward Autonomous Physical Security Defenses Using Machine Learning

Toward Autonomous Physical Security Defenses Using Machine Learning

The sheer increase in interconnected devices, reaching 50 B in 2025, makes it easier for adversaries to have direct access to the target system and perform physical attacks. This risk is exacerbated by the proliferation of Internet-of-Battlefield Things (IoBT) and increased reliance on the use of em...

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Bibliographic Details
Main Authors: Basel Halak, Christian Hall, Syed Fathir, Nelson Kit, Ruwaydah Raymode, Michael Gimson, Ahmad Kida, Hugo Vincent
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Anti-tamper design
internet of battlefield things (IoBT)
machine learning algorithms
adversarial machine learning
physical attacks
hardware security
Online Access:https://ieeexplore.ieee.org/document/9775706/

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