Network intrusion detection systems (NIDSs) play an important role in computer network
security. There are several detection mechanisms where anomaly-based automated …

As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems
(IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or …

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

Network Intrusion Detection is one of the most researched topics in the field of computer
security. Hacktivists use sophisticated tools to launch numerous attacks that hamper the …

Abstract A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)
requires a large amount of labeled up-to-date training data to effectively detect intrusions …

Feature selection is becoming a major preprocessing phase in which irrelevant and
redundant features are removed, while the more informative ones are retained. The datasets …

Through the ongoing digitization of the world, the number of connected devices is
continuously growing without any foreseen decline in the near future. In particular, these …

The network intrusion detection system is an important tool for protecting computer networks
against threats and malicious attacks. Many techniques have recently been proposed; …

This work proposes an end-to-end model for network attack detection and network attack
classification using deep learning-based recurrent models. The proposed model extracts the …

With exponential growth in the number of computer applications and the sizes of networks,
the potential damage that can be caused by attacks launched over the Internet keeps …