The identification of network attacks which target information and communication systems
has been a focus of the research community for years. Network intrusion detection is a …

This paper reviews the background and related studies in the areas of cloud systems,
intrusion detection and blockchain applications against cyber attacks. This work aims to …

A great deal of attention has been given to deep learning over the past several years, and
new deep learning techniques are emerging with improved functionality. Many computer …

Anomalies could be the threats to the network that have ever/never happened. To protect
networks against malicious access is always challenging even though it has been studied …

Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial
neural networks. It is the de‐facto standard for various operations in machine learning and …

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering
numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for …

Deep learning has been widely applied to network anomaly detection to improve
performance. In our past research, we empirically evaluated a set of deep learning models …

In this paper, to solve the problem of detecting network anomalies, a method of forming a set
of informative features formalizing the normal and anomalous behavior of the system on the …

This is a systematic review of over one hundred research papers about machine learning
methods applied to defensive and offensive cybersecurity. In contrast to previous reviews …

Intrusion detection is one essential tool towards building secure and trustworthy Cloud
computing environment, given the ubiquitous presence of cyber attacks that proliferate …