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

Providing a high-performance Intrusion Detection System (IDS) can be very effective in
controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative …

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems,
and distributed control systems (DCSs) are fundamental components of critical infrastructure …

Abstract Anomaly-based Intrusion Detection System (IDS) has been a hot research topic
because of its ability to detect new threats rather than only memorized signatures threats of …

Introduction: The growth of ubiquitous networked devices and the proliferation of
geographically dispersed 'Internet of Thing'devices have exponentially increased network …

In this paper, an intrusion detection system is introduced that uses data mining and machine
learning concepts to detect network intrusion patterns. In the proposed method, an artificial …

Abstract Information Extraction has been widely used to extract information from text. Named
Entity Recognition (NER) is one of the primary tasks of Information Extraction to extract …

This research presents a new generic deep learning (DL) framework for anomaly detection
in the Internet of Everything (IoE). It combines decomposition methods, deep neural …

The rate of internet traffic in the digital world has expanded fast due to technological
improvement. Because of the large number of internet users, there is a large volume of …

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks
Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias …