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 …

Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play
a crucial role in algorithms performance. It reduces the processing time and accuracy of the …

The development and expansion of the Internet and cyberspace have increased computer
systems attacks; therefore, Intrusion Detection Systems (IDSs) are needed more than ever …

Feature selection (FS) is considered to be a hard optimization problem in data mining and
some artificial intelligence fields. It is a process where rather than studying all of the features …

Traditional ML based IDS cannot handle high-speed and ever-evolving attacks.
Furthermore, these traditional IDS face several common challenges, such as processing …

Intrusion detection system (IDS) has been developed to protect the resources in the network
from different types of threats. Existing IDS methods can be classified as either anomaly …

The modern industrial control systems now exhibit an increasing connectivity to the
corporate Internet technology networks so as to make full use of the rich resource on the …

Many metaheuristic approaches have been developed to select effective features from
different medical datasets in a feasible time. However, most of them cannot scale well to …

Machine learning and data mining techniques have been widely used in order to improve
network intrusion detection in recent years. These techniques make it possible to automate …