A New Mobile Malware Classification for Audio Exploitation - Nur Arif, Muhamad; Abu Bakar, Azreena; Mohd Saudi, Madihah (International Journal of Engineering and Technology, 2018)
Rapid growth and usage of Android smartphones worldwide have attracted many attackers to exploit them. Currently, the attackers used mobile malware to attack victimsâ€™ smartphones to steal confidential information such as username and password. The attacks are also motivated based on profit and money. The attacks come in different ways, such as via audio, image, GPS location, SMS and call logs in the smartphones. Hence, this paper presents a new mobile malware classification for audio exploitation. This classification is beneficial as an input or database to detect the mobile malware attacks. System calls and permissions for audio exploitation have been extracted by using static and dynamic analyses using open source tools and freeware in a controlled lab environment. The testing was conducted by using Drebin dataset as the training dataset and 500 anonymous apps from Google Play store as the testing dataset. The experiment results showed that 2% suspicious malicious apps matched with the proposed classification. The finding of this paper can be used as guidance and reference for other researchers with the same interest.Â Â