The competing landscape between malware authors and security analysts is an ever‐
changing battlefield over who can innovate over the other. While security analysts are …

Abstract Distributed Denial of Service (DDoS) attack is a stubborn network security problem.
Various machine learning-based methods have been proposed to detect such attacks …

With the rapid expansion of intelligent resource-constrained devices and high-speed
communication technologies, the Internet of Things (IoT) has earned wide recognition as the …

Cloud computing has very attractive features like elastic, on demand and fully managed
computer system resources and services. However, due to its distributed and dynamic …

Distributed denial of service (DDoS) attack is an attempt to make an online service
unavailable by overwhelming it with traffic from multiple sources. Therefore, it is necessary to …

The hacker attempts distributed denial of service (DDoS) attacks towards network resources
to disturb or deny services. The hacker degrades the quality of service to legitimate users by …

In this study, the authors propose a multi‐objective optimisation‐based feature selection
(FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of …

The number of devices connected to the Internet is increasing day by day. This increase
causes cyber-attacks to be larger and more complex. It is important to sdetect the anomalies …

This paper applies an organized flow of feature engineering and machine learning to detect
distributed denial-of-service (DDoS) attacks. Feature engineering has a focus to obtain the …

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated
to a high index of mortality. One of its major complications is diabetic foot, leading to plantar …