Machine learning models have made many decision support systems to be faster, more
accurate, and more efficient. However, applications of machine learning in network security …

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks
that break every ML model, or defenses that withstand most attacks. Unfortunately, little …

Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from
adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks …

Phishing is one of the most popular cyberspace attacks. Phishing detection has been
integrated into mainstream browsers to provide online protection. The phishing detector of …

Malicious advertisement URLs pose a security risk since they are the source of cyber-
attacks, and the need to address this issue is growing in both industry and academia …

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks
that break every ML model or defenses that withstand most attacks. Unfortunately, little …

Graph Convolution Networks (GCNs) are neural networks that can be used to perform
different kinds of analysis and mining activities on graphs. GCNs are neural networks that …

State-of-the-art deep learning models for tabular data have recently achieved acceptable
performance to be deployed in industrial settings. However, the robustness of these models …

Deep neural networks (DNNs) have shown great promise in various domains. Alongside
these developments, vulnerabilities associated with DNN training, such as backdoor attacks …

Machine learning models are under severe threat by adversarial attacks. Machine learning
is swiftly taking on a central role in organizations' value propositions, and as a result …