Mobile Malware Behaviour through Opcode Analysis

Abstract

As the popularity of mobile devices are on the rise, millions of users are now exposed to mobile malware threats. Malware is known for its ability in causing damage to mobile devices. Attackers often use it as a way to use the resources available and for other cybercriminal benefits such stealing users’ data, credentials and credit card number. Various detection techniques have been introduced in mitigating mobile malware, yet the malware author has its own method to overcome the detection method. This paper presents mobile malware analysis approaches through opcode analysis. Opcode analysis on mobile malware reveals the behaviour of malicious application in the binary level. The comparison made between the numbers of opcode occurrence from a malicious application and benign shows a significance traits. These differences can be used in classifying the malicious and benign mobile application.

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

“…An alternative audit source data to overcome this drawback is through dynamic analysis. [25], [26], [27], [28] and [29] are the researchers that used dynamic audit data source features such as Network traffic traces, systems call, user interaction behaviors and opcode. This type of audit data source can evade the code obfuscation and encryption as the mobile application activity traces are captured during the runtime of the application.…”