Network traffic classification to detect DDoS attacks is challenging in the context of high-
speed networks. In this paper, we discuss the need for distributed feature selection in …

As more organizations and businesses in different sectors are moving to a digital
transformation, there is a steady increase in malware, facing data theft or service …

A distributed denial-of-service (DDoS) and DoS attack are the most devastating and
expensive attacks among various cyber and network attacks [1][2]. Coupled with the fact that …

Heaps of Data lie in network equipment of the organizations. To break down this information
and reach some significant inferences is inconceivable for the present day IDS (Intrusion …

The use of supervised learning algorithms to detect malicious traffic can be valuable in
designing intrusion detection systems and ascertaining security risks. The Internet of things …

Bidirectional communication infrastructure of smart systems, such as smart grids, are
vulnerable to network attacks like distributed denial of services (DDoS) and can be a major …

Two supervised learning algorithms, a basic neural network and a long short‐term memory
recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of …

In the current cyber world, one of the most severe cyber threats are distributed denial of
service (DDoS) attacks, which make websites and other online resources unavailable to …

Abstract Even though advanced Machine Learning (ML) techniques have been adopted for
DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML …

In this research, we explore the performances of two supervised learning techniques and
two open-source network intrusion detection systems (NIDS) on backscatter darknet traffic …