With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

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 …

Android receives major attention from security practitioners and researchers due to the influx
number of malicious applications. For the past twelve years, Android malicious applications …

VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily
used by researchers for malware annotation and system evaluation. Since different engines …

As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency
exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the …

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 …

Attributing a piece of malware to its creator typically requires threat intelligence. Binary
attribution increases the level of difficulty as it mostly relies upon the ability to disassemble …

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

Labeling malware or malware clustering is important for identifying new security threats,
triaging and building reference datasets. The state-of-the-art Android malware clustering …

Malware is a major threat to computer systems and imposes many challenges to cyber
security. Targeted threats, such as ransomware, cause millions of dollars in losses every …