High Accuracy Detection of Mobile Malware Using Machine Learning

As smartphones and other mobile and IoT devices have become pervasive in everyday life, malicious software (malware) authors are increasingly targeting the operating systems that are at the core of these mobile systems. Malware targeting mobile platforms has witnessed an explosive growth in the last decade. As a result of this rapid increase in mobile malware, the limits of traditional signature-based antivirus scanning have been stretched. This has led to the emergence of machine learning-based detection as a complementary solution to traditional antivirus scanning. Although machine learning-based malware detection has continued to attract great research interest, many challenges remain as emerging malware families continue to evolve with more sophisticated capabilities and stealthy evasive techniques. This Special Issue in Electronics presents some of the most recent research results and innovative machine learning-based approaches to detecting malicious software and attacks that can compromise mobile platforms.