Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …

Nowadays, the ever-increasing complication and severity of security attacks on computer
networks have inspired security researchers to incorporate different machine learning …

The application of new techniques to increase the performance of intrusion detection
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has
taken a prominent role in the network security management field, due to the substantial …

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart
healthcare and will continue to be impactful in medical infrastructures in the near future. This …

Smart grid systems enhanced the capability of traditional power networks while being
vulnerable to different types of cyber‐attacks. These vulnerabilities could cause attackers to …

The competing landscape between malware authors and security analysts is an ever‐
changing battlefield over who can innovate over the other. While security analysts are …

Cloud computing (CC) is the fastest-growing data hosting and computational technology that
stands today as a satisfactory answer to the problem of data storage and computing …

A rapid development of internet technology brings convenience to society and threat of
exploitation at the same time. As a countermeasure, an Intrusion Detection System (IDS) …

Detection of evolving cyber attacks is a challenging task for conventional network intrusion
detection techniques. Various supervised machine learning algorithms have been …