Machine learning (ML)-based Android malware detection has been one of the most popular
research topics in the mobile security community. An increasing number of research studies …

Deep learning classifiers achieve state-of-the-art performance in various risk detection
applications. They explore rich semantic representations and are supposed to automatically …

Smartphones have started to take an essential place in every aspect of our lives with the
developing technology. All kinds of transactions, from daily routine work to business …

Cognitive security is the interception between cognitive science and artificial intelligence
techniques used to protect institutions against cyberattacks. However, this field has not been …

Recently there is an upsurge in Android malware that use obfuscation and repackaging
techniques for evasion. Malware may also combine both these techniques to create stealthy …

Software debloating techniques are applied to craft a specialized version of the program
based on the user's requirements and remove irrelevant code accordingly. The debloated …

Machine Learning (ML) models have been utilized for malware detection for over two
decades. Consequently, this ignited an ongoing arms race between malware authors and …

Many approaches for Android malware detection have been proposed to combat against the
rise in mobile malware–most of which have relied on machine learning [3, 10, 17]. Such …

Mobile smart phones have become increasingly popular in the past decade. As a matter of
fact, the number of smart phone users are expected to nearly double from 2016 to 2021 [17] …

Different approaches have been proposed to generate Android malware detection systems.
Some of these approaches use Machine learning methods by first statically extracting …