Machine learning has witnessed tremendous growth in its adoption and advancement in the
last decade. The evolution of machine learning from traditional algorithms to modern deep …

Machine learning based solutions have been very helpful in solving problems that deal with
immense amounts of data, such as malware detection and classification. However, deep …

The widespread adoption of smartphones dramatically increases the risk of attacks and the
spread of mobile malware, especially on the Android platform. Machine learning-based …

Cyber-security is the practice of protecting computing systems and networks from digital
attacks, which are a rising concern in the Information Age. With the growing pace at which …

Malicious software (malware) is a major cyber threat that has to be tackled with Machine
Learning (ML) techniques because millions of new malware examples are injected into …

The convolutional neural network (CNN) architecture is increasingly being applied to new
domains, such as malware detection, where it is able to learn malicious behavior from raw …

Machine learning-based malware detection is known to be vulnerable to adversarial
evasion attacks. The state-of-the-art is that there are no effective defenses against these …

In recent years, machine learning algorithms, and more specifically deep learning
algorithms, have been widely used in many fields, including cyber security. However …

Malware is constantly evolving with rising concern for cyberspace. Deep learning-based
malware detectors are being used as a potential solution. However, these detectors are …

Despite many attempts, the state-of-the-art of adversarial machine learning on malware
detection systems generally yield unexecutable samples. In this work, we set out to examine …