In this review, significant literature surveys on machine learning (ML) and deep learning
(DL) techniques for network analysis of intrusion detection are explained. In addition, it …

It is an undeniable fact that currently information is a pretty significant presence for all
companies or organizations. Therefore protecting its security is crucial and the security …

The development of smart network infrastructure of the Internet of Things (IoT) faces the
immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The …

In this research, we analyzed the suitability of each of the current state-of-the-art machine
learning models for various cyberattack detection from the past 5 years with a major …

Existing distributed denial-of-service attack detection in software defined networks (SDNs)
typically perform detection in a single domain. In reality, abnormal traffic usually affects …

The characteristics of the mobile ad hoc network (MANET), such as no need for
infrastructure, high speed in setting up the network, and no need for centralized …

Internet of Things is one of the most versatile technologies in existence today. It has taken
over our day to day activities and thus has many applications that are designed to make life …

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 …

The growing number of IoT edge devices have inflicted a change in the cyber-attack space.
The DDoS attacks, in particular, have significantly increased in magnitude and intensity. Of …

Adaptive detection is the learning ability to detect any changes in patterns in intrusion
detection systems. In this paper, we propose combining two techniques in feature selection …