Many security applications require unsupervised anomaly detection, as malicious data are
extremely rare and often only unlabeled normal data are available for training (ie, zero …

The development of IoT systems combined with the smart environments work in an effective
way by making the objects to be smart. On the other hand, the IoT systems are vulnerable to …

The increasing prevalence of unknown-type attacks on the Internet highlights the importance
of developing efficient intrusion detection systems. While machine learning-based …

The evolution of Internet-of-Things (IoT) is fostering the use of intelligent controls for energy
conservation. Yet, the efficacy of these strategies is largely tied to diverse load forecasting …

The prevalence of malware on the Internet makes malware detection vital as an early
warning system for organizations' security. This paper presents a novel approach to linking …

With the rapid development of Internet of things (IoT), an increasing volume of data is
generated across diverse IoT devices. Within these data, an extremely limited subset may …

Early detection is crucial to mitigate the impact of emerging threats. This work proposes four
innovative frameworks that build machine learning and deterministic epidemiological …

As global Internet of Things (IoT) devices connectivity surges, a significant portion gravitates
towards the Edge of Things (EoT) network. This shift prompts businesses to deploy …

The IoT offered an enormous number of services with the help of multiple applications so it
faces various security-related problems and also heavy malicious attacks. Initially, the IoT …

Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to
detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput …