The cybersecurity concerns get increasingly intricate as the digital world progresses. In light
of the increasing complexity of cyber threats, it is imperative to develop and implement …

With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

Abstract The rise of Adversarial Machine Learning (AML) attacks is presenting a significant
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …

Deep neural networks have been found to be vulnerable in a variety of tasks. Adversarial
attacks can manipulate network outputs, resulting in incorrect predictions. Adversarial …

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G)
networks due to dense connectivity, ultrareliability, low latency, and high throughput …

As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical
ethical issue that must be addressed. This article explores the need for ethical AI systems …

Abstract The Internet of Things (IoT) paradigm and the advanced wireless technologies of
5G and beyond are expected to enable diverse applications such as autonomous driving …

Deep neural network (DNN) classifiers are potent instruments that can be used in various
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …

Cyber deception has emerged as a valuable technique in the field of cybersecurity, closely
linked with adversarial Artificial Intelligence. In an era of pervasive automation, it is getting …