Most of the data manipulation attacks on deep neural networks (DNNs) during the training
stage introduce a perceptible noise that can be catered by preprocessing during inference …

Over the past years, numerous studies demonstrated the vulnerability of deep neural
networks (DNNs) to make correct classifications in the presence of small noise. This …

Traditional learning-based approaches for runtime hardware Trojan (HT) detection require
complex and expensive on-chip data acquisition frameworks, and thus incur high area and …

Private or special-purpose wireless networks present a new technological trend for future
mobile communications, while one attractive application scenario is the wireless …

The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …

The security of Industrial Control Systems is relevant both for reliable production system
operations and for high-quality throughput in terms of manufactured products. Security …

We propose a versatile and fully data-centric methodology towards anomaly detection and
identification in modern industrial Cyber–Physical Systems (CPS). Our motivation behind …

Abstract Machine intelligence, aka artificial intelligence (AI) is one of the most prominent and
relevant technologies today. It is in everyday use in the form of AI applications and has a …

In the past years, great effort has been spent on enhancing the security and safety of
vehicular systems. Current advances in information and communication technology have …

Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where
billions of physical devices are interconnected to provide data sensing, computing and …