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Title	A Comprehensive Review On Embedded Systems Security Using Machine Learning
ID_Doc	938
Authors	Podder D.; Sengupta Y.
Year	2025
Published	Embedded Artificial Intelligence: Real-Life Applications and Case Studies
DOI	http://dx.doi.org/10.1201/9781003481089-11
Abstract	The market for embedded and integrated systems is expanding rapidly, and this has led to an increase in cybersecurity requirements and concerns across a wide range of industries, including smart cities, IoT, industrial-IoT, healthcare, automotive, armed forces, and remote intelligence, to mention a few. Over time, the end devices have evolved to offer higher-end user services. Because of the distributed development culture in the semiconductor industry, as well as the high level of sophistication of connected device architecture in many forms, including Vehicle-to-Anything, threat actors now have an easier time injecting and exploiting vulnerabilities that typically pass unseen from the verification phases or are introduced in the design. It is necessary to implement cybersecurity verification mechanisms to address the expanding attack surfaces and vulnerabilities. These mechanisms should detect, manage, and prevent the obsolescence of equipment manufactured because of security flaws, as well as safeguard user data to preserve confidentiality and privacy. But often, the volume of data is a significant challenge as well, necessitating the use of Artificial Intelligence and Machine Learning. © 2025 selection and editorial matter, Arpita Nath Boruah, Mrinal Goswami, Manoj Kumar, Octavio Loyola-González; individual chapters, the contributors.
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