Year after year, mobile malware attacks grow in both sophistication and diffusion. As the
open source Android platform continues to dominate the market, malware writers consider it …

Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Zero-day malware samples pose a considerable danger to users as implicitly there are no
documented defences for previously unseen, newly encountered behaviour. Malware …

Malware detection at scale in the Android realm is often carried out using machine learning
techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to …

Existing Android malware detection methods are usually hard to simultaneously resist
various obfuscation techniques. Therefore, bytecode-based code obfuscation becomes an …

With approximately 2 billion active devices, the Android operating system tops all other
operating systems in terms of the number of devices using it. Android has gained wide …

A challenge for implementing deep learning research in the real-world is the availability of
techniques that explain predictions of a model, particularly in light of potential legal …

With the rapid growth of Android devices and applications, the Android environment faces
more security threats. Malicious applications stealing usersʼ privacy information, sending …

Android OS popularity has given significant rise to malicious apps targeting it. Malware use
state of the art obfuscation methods to hide their functionality and evade anti-malware …

The various application markets are facing an exponential growth of Android malware. Every
day, thousands of new Android malware applications emerge. Android malware hackers …