Secure and Trusted Open CPS Platforms

Kornaros, George; Wozniak, Ernest; Horst, Oliver; Koch, Nora; Prehofer, Christian; Rigo, Alvise; Coppola, Marcello

doi:10.4018/978-1-5225-2845-6.ch012

Cited by 5 publications

(1 citation statement)

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“…Nonetheless, IoT devices are mostly restricted devices with inadequate tamper-resistant and tamper-detection methods, allowing connected devices to leak personal data, for example, by allowing modified firmware to access authentication credentials. With the rapid proliferation of Internet of Things (IoT) devices and cloud systems, most cyber-physical systems' quickly increasing attack surface can scarcely keep attacks from multiplying in quantity and sophistication, as seen in Fig 1 . IoT-enabled cyber-physical systems (CPSs) in factories, smart grids, and automobiles now have a variety of communication interfaces, remote monitoring, and software-over-the-air administration capabilities [2] [15]. In essence, Industrial IoT (IIoT) networks have a broad attack surface, making a covert channel more difficult to detect.…”

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confidence: 99%

Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective

Kornaros¹

2022

IEEE Access

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As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart devices act in concert, rarely requiring human intervention, posing significant challenges in supporting trusted computing and user privacy, as well as protecting against attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping. To tackle attacks on the IoT and cyber-physical ecosystem, many intrusion detection and security approaches have been presented in the literature. Machine learning (ML) based intrusion and anomaly detection has lately gained traction due to its capacity to cope with encrypted and rapidly developing threat techniques. This work investigates into machine learning (ML) and deep learning (DL) methodologies for IoT device security and examine the benefits, drawbacks, and potential. To protect an IoT infrastructure, various solutions look into hardware-based methods for ML-based IoT authentication, access control, secure offloading, and malware detection schemes. This review aims to illuminate the value of various approaches for addressing IoT security in a truly effective, flexible, and seamless manner, as well as to provide answers to questions about tradeoffs in integrating accelerators and customizing embedded device architectures for effective use of ML-based methods. INDEX TERMSAI-based IoT security, Hardware-based machine learning, IoT intrusion detection, Trusted embedded devices I. INTRODUCTION

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confidence: 99%