With the usage of Machine Learning (ML) algorithms in modern-day Network Intrusion
Detection Systems (NIDS), contemporary network communications are efficiently protected …

Software defined network (SDN) has emerged as a new paradigm in terms of network
architecture, providing flexibility, agility, and programmability to network management …

Machine learning (ML)-based network intrusion detection systems (NIDS) have become a
prospective approach to efficiently protect network communications. However, ML models …

Abstract The Internet of Things (IoT) connects people, devices, and processes in multiple
ways, resulting in the rapid transformation of several industries. Apart from several positive …

Distributed Denial-of-Service (DDoS) attacks have always posed a major threat to networks
directly or as a cover for more sophisticated attacks. In recent years, with advances such as …

Modern software-defined networks (SDN) provide additional control and optimal
functionality over large-scale computer networks. Due to the rise in networking applications …

Machine learning methods have solid accuracy in detecting cyber attacks at the network
layer. Nevertheless, the per-packet detection model is not scalable for high-speed networks …

Machine learning (ML)-based network intrusion detection systems (NIDS) have become a
contemporary approach to efficiently protect network communications from cyber attacks …

With an escalating reliance on sensor-driven scientific endeavors in challenging terrains, the
significance of robust hybrid networks, formed by a combination of wireless and wired links …

SYN Flood malicious traffic attack is a typical representative of distributed denial of service
(DDo S) attacks and one of the primary factors threatening IoT security. In response to the …