Mobile Malware Detection using Machine Learning Classifiers

In today world the mobile malware shows the significant threat to the security and privacy of the society using smartphones. These malware aims to access the sensitive data and harm the devices of users. This paper conducts a comprehensive comparison between the various machine learning and traditional methods for mobile malware detection based on the research papers published by the authors. Signature-based detection depends upon the predefined and common patterns, while the anomaly based techniques analyse the deviation from the regular normal behaviour. This study discusses the strengths and limitations of different approaches and highlights the need for adopting the malware detection methods to fight the growing threats. It also examines the role of machine learning algorithms, like Decision Trees, Random Forests, Convolutional Neural Networks, Support Vector Machines, and Naïve Bayes, for better malware detection. Latest findings and research highlights the importance of the continuing innovation to fight the emerging threat to the user privacy, data and security due to malwares.