Network intrusion detection is a key pillar towards the sustainability and normal operation of
information systems. Complex threat patterns and malicious actors are able to cause severe …

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 …

Network traffic anomaly detection is an important technique of ensuring network security.
However, there are usually three problems with existing machine learning based anomaly …

With the explosion of network traffic volume, high efficient and large-scale network traffic
anomaly detection methods becomes necessary. However, existing methods often fail to …

Deep generative models have increasingly become popular in different domains such as
image processing, though, they hardly appear in the cybersecurity arena. While the main …

Machine learning models using deep architectures (ie, deep learning) have gained path in
recent years and have become state-of-the-art in many fields, including computer vision …

In this paper, an intelligent and lightweight anomalous network traffic detection framework is
proposed. The framework uses principal component analysis (PCA) with the main purpose …

In order to study the application of deep learning in the design of network traffic anomaly
detection device, aiming at two common problems in the field of network anomaly detection …

The availability of an enormous amount of unlabeled datasets drives the anomaly detection
research towards unsupervised machine learning algorithms. Deep clustering algorithms for …

Artificial intelligence-assisted security is an important field of research in relation to
information security. One of the most important tasks is to distinguish between normal and …