Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly
expanding field. This growth necessitates an examination of application trends and current …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

In the past several years, the world has witnessed an acute surge in the production and
usage of smart devices which are referred to as the Internet of Things (IoT). These devices …

Cloud computing provides scalable and elastic resources to customers as a low-cost, on-
demand utility service. Multivariate time series anomaly detection is crucial to promise the …

Deep learning (DL) techniques are being widely researched for their effectiveness in
detecting cyber intrusions against the Internet of Things (IoT). Time sensitive Critical …

We propose an integrated framework for an intrusion detection system for SCADA
(Supervisory Control and Data Acquisition)-based power grids. Our scheme combines RFE …

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

Abstract Currently, the Internet of Things is spreading in all areas that apply computing
resources. An important ally of the IoT is fog computing. It extends cloud computing and …

Since network intrusion detectors for the Internet of Things (IoT) increasingly rely on
machine learning models, attacks against these detectors are also escalating. Machine …

The data generated exponentially by a massive number of devices in the Internet of Things
(IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great …