With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …

With the emergence of the Internet of Things (IoT), the computer networks' phenomenal
expansion, and enormous relevant applications, data is continuously increasing. In this way …

A Network Intrusion Detection System (NIDS) helps system administrators to detect network
security breaches in their organizations. However, many challenges arise while developing …

Handling redundant and irrelevant features in high-dimension datasets has caused a long-
term challenge for network anomaly detection. Eliminating such features with spectral …

With increasing reliance on Internet of Things (IoT) devices and services, the capability to
detect intrusions and malicious activities within IoT networks is critical for resilience of the …

In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has
been more complicated owing to developments in the internet and devices' connectivity. To …

Redundant and irrelevant features in data have caused a long-term problem in network
traffic classification. These features not only slow down the process of classification but also …

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 …

A novel supervised machine learning system is developed to classify network traffic whether
it is malicious or benign. To find the best model considering detection success rate …

Deep learning methods, eg, convolutional neural networks (CNNs) and Recurrent Neural
Networks (RNNs), have achieved great success in image processing and natural language …