The selection of machine learning tools for data analytics might be challenging due to the
ever-growing number of alternatives. The various tools each have benefits and limitations …

Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last
decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight …

Since traditional machine learning (ML) techniques use black-box model, the internal
operation of the classifier is unknown to human. Due to this black-box nature of the ML …

In recent years machine learning (ML) systems have been deployed extensively in various
domains. But most MLbased frameworks lack transparency. To believe in ML models, an …

Over the past two decades, Distributed Denial of Service (DDoS) attacks have been
responsible for most of the catastrophic failures in the Internet causing a huge amount of …

Abstract Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-
physical system over the last decade. Defending against DDoS attack is not only …

We propose an intrusion detection system (NLPIDS) that utilizes natural language
processing and ensemble-based machine learning. The proposed NLPIDS converts natural …

Today's software systems tend to be long-living and often process security-critical data, so
keeping up with ever-changing security measures, attacks, and mitigations is critical to …

Distributed denial of service (DDoS) is a network-based attack where the aim of the attacker
is to overwhelm the victim server. The attacker floods the server by sending enormous …

A stealth migration protocol is proposed in this paper that obfuscates the virtual machine
(VM) migration from intruders and enhances the security of the MTD process. Starting by …