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 …

With the integration of mobile devices into daily life, smartphones are privy to increasing
amounts of sensitive information. Sophisticated mobile malware, particularly Android …

The alarming growth rate of malicious apps has become a serious issue that sets back the
prosperous mobile ecosystem. A recent report indicates that a new malicious app for …

Machine learning (ML) classifiers have been widely deployed to detect Android malware,
but at the same time the application of ML classifiers also faces an emerging problem. The …

Most existing Android malware detection and categorization techniques are static
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …

App Store Analysis studies information about applications obtained from app stores. App
stores provide a wealth of information derived from users that would not exist had the …

As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

Context Static analysis exploits techniques that parse program source code or bytecode,
often traversing program paths to check some program properties. Static analysis …

The rise in popularity of the Android platform has resulted in an explosion of malware threats
targeting it. As both Android malware and the operating system itself constantly evolve, it is …

Internet of Things (IoT) platforms often require users to grant permissions to third-party apps,
such as the ability to control a lock. Unfortunately, because few users act based upon, or …