Opcode n-gram based Malware Classification in Android
Vikas Kumar Sihag, Anita Mitharwal, Manu Vardhan, Pradeep Kumar Singh · 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · 2020
Smartphone's pervasive presence has offered new possibilities to life experiences, with its power to compute, sense, connect and to be mobile. Android OS since its release in 2008, has grown as the most preferred choice in the market and thus popular among app developers. Detecting nature of an application (benign or malicious) is an ever-evolving research problem. Detection and containment of malicious apps by hosting platforms is an important security consideration [8], [10], [13]. Malware like Geinimi and DroidDream pursuits to collect personal identifiable information, privilege escalation and perform financial operations. In this paper we propose a opcode based approach for malware familial classification. It uses bloom filter for similarity calculation among n-gram based attributes.