Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …

The smart grid (SG), regarded as the complex cyber-physical ecosystem of infrastructures,
orchestrates advanced communication, computation, and control technologies to interact …

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Smart Grids (SGs) are governed by advanced computing, control technologies, and
networking infrastructure. However, compromised cybersecurity of the smart grid not only …

Machine learning (ML)-based intrusion detection system (IDS) approaches have been
significantly applied and advanced the state-of-the-art system security and defense …

As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …

The recent developments in deep learning techniques evolved to new heights in various
domains and applications. The recognition, translation, and video generation of Sign …

We propose a unified machine learning (ML) framework for simultaneously performing
electrical load forecasting and unsupervised anomaly detection in real time. For load …

Despite artificial intelligence (AI)'s significant growth, its “black box” nature creates
challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in …