The recent developments in deep learning techniques evolved to new heights in various
domains and applications. The recognition, translation, and video generation of Sign …

Develo** an effective classification model in the medical field is challenging due to limited
datasets. To address this issue, this study proposes using a generative adversarial network …

Abstract Fast Flux Botnet (FFB) is an advance method developed by cyber criminals to
perpetrate distributed malicious attacks. The major problems of existing FFB detection …

Abstract Recently, Software Defined Networking (SDN) has emerged as the key technology
in programming and orchestrating security policy in the security operations centers (SOCs) …

With the proliferation of Artificial Intelligence, there has been a massive increase in the
amount of data required to be accumulated and disseminated digitally. As the data are …

The digitization trend that prevails nowadays has led to increased vulnerabilities of tools and
technologies of everyday life. One of the many different types of software vulnerabilities and …

A myriad of recent literary works have leveraged generative adversarial networks (GANs) to
generate unseen evasion samples. The purpose is to annex the generated data with the …

Botnet detectors based on machine learning are potential targets for adversarial evasion
attacks. Several research works employ adversarial training with samples generated from …

This paper investigates the efficacy of the Super Learner ensemble algorithm for robust
anomaly detection in Internet of Things (IoT) network traffic. The recently released CIC IoT …

Intrusion detection systems face significant challenges in handling high-dimensional, large-
scale, and imbalanced network traffic data. This paper proposes a new architecture …