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

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics.
The data with a large number of features will affect the computational complexity, increase a …

Network intrusion detection systems (NIDSs) provide a better solution to network security
than other traditional network defense technologies, such as firewall systems. The success …

This study investigates the performance of two open source intrusion detection systems
(IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer …

Software defined Internet of Things (SD-IoT) networks profit from centralized management
and interactive resource sharing, which enhances the efficiency and scalability of Internet of …

In the era of big data, with the increasing number of audit data features, human-centered
smart intrusion detection system performance is decreasing in training time and …

The diversity and complexity of network data bring great challenges to data classification
technology. Feature selection has always been an important and difficult problem in …

The growth of data and categories of attacks, demand the use of Intrusion Detection System
(IDS) effectively using Machine Learning (ML) and Deep Learning (DL) techniques. Apart …

Emerging applications---cloud computing, the internet of things, and augmented/virtual
reality---demand responsive, secure, and scalable datacenter networks. These networks …