Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

A popular research area is internet traffic analysis as it has many applications, mainly for
classifying internet traffic. Innovative technologies have been developed for predicting and …

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays
involve many electronic control units connected through intravehicle networks (IVNs) to …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an …

The security of networked systems has become a critical universal issue that influences
individuals, enterprises and governments. The rate of attacks against networked systems …

The adoption of machine learning and deep learning is on the rise in the cybersecurity
domain where these AI methods help strengthen traditional system monitoring and threat …

Various attacks have emerged as the major threats to the success of a connected world like
the Internet of Things (IoT), in which billions of devices interact with each other to facilitate …

Conventional intrusion detection systems based on supervised learning techniques require
a large number of samples for training, while in some scenarios, such as zero-day attacks …

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …