Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

The struggle between security analysts and malware developers is a never-ending battle
with the complexity of malware changing as quickly as innovation grows. Current state-of-the …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

Malware detection approaches can be classified into two classes, including static analysis
and dynamic analysis. Conventional approaches of the two classes have their respective …

This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …

Security breaches due to attacks by malicious software (malware) continue to escalate
posing a major security concern in this digital age. With many computer users, corporations …

This paper describes EMBER: a labeled benchmark dataset for training machine learning
models to statically detect malicious Windows portable executable files. The dataset …