This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

In recent years, machine learning-based intrusion detection systems (IDSs) have proven to
be effective; especially, deep neural networks improve the detection rates of intrusion …

Intrusion detection is an important technique that can provide solid protection for the network
equipment against the security attacks. However, the attacks are usually unbalanced in …

The digital revolution has substantially changed our lives in which Internet-of-Things (IoT)
plays a prominent role. The rapid development of IoT to most corners of life, however, leads …

Internet of Things (IoT) has emerged as a cutting-edge technology that is changing human
life. The rapid and widespread applications of IoT, however, make cyberspace more …

Due to the outbreak of recent network attacks, it is necessary to develop a robust network
intrusion detection system (NIDS) that can quickly and effectively identify the network attack …

Anomaly detection methods based on machine learning assist in identifying attacker
behavior concealed in critical infrastructure's high-speed network traffic. However, these …

With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming
an important and essential tool to protect Internet of Things (IoT) environments. However, it …

Marine sensors are highly vulnerable to illegal access network attacks. Moreover, the
nation's meteorological and hydrological information is at ever-increasing risk, which calls …