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 …

The future Internet of Things (IoT) will have a deep economical, commercial and social
impact on our lives. The participating nodes in IoT networks are usually resource …

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

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

Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

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

Abstract Machine learning models are often susceptible to adversarial perturbations of their
inputs. Even small perturbations can cause state-of-the-art classifiers with high" standard" …

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 …

Machine learning has already been exploited as a useful tool for detecting malicious
executable files. Data retrieved from malware samples, such as header fields, instruction …

Network defenses based on traditional tools, techniques, and procedures (TTP) fail to
account for the attacker's inherent advantage present due to the static nature of network …