Adversarial attacks have threatened the credibility of machine learning models and cast
doubts over the integrity of data. The attacks have created much harm in the fields of …

Since network intrusion detectors for the Internet of Things (IoT) increasingly rely on
machine learning models, attacks against these detectors are also escalating. Machine …

Adversarial attacks have been widely studied in the field of computer vision but their impact
on network security applications remains an area of open research. As IoT, 5G and AI …

During the last decade, the integration of artificial intelligence (AI) and the use of intrusion
detection systems (IDSs) in the Internet of Things (IoT) networks have brought a new …

As the internet continues to be populated with new devices and emerging technologies, the
attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of …

Deep learning (DL) applications in network intrusion detection systems (NIDS) are
increasingly popular in protecting IoT networks against cyber threats. However, these …

Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have
been widely deployed in the real world. However, DNNs are found to be vulnerable to …

Studies have shown the vulnerability of machine learning algorithms against adversarial
samples in image classification problems in deep neural networks. However, there is a need …

Adversarial machine learning is a recent area of study that explores both adversarial attack
strategy and detection systems of adversarial attacks, which are inputs specially crafted to …

Using deep learning networks, anomaly detection systems have seen better performance
and precision. However, adversarial examples render deep learning-based anomaly …