CogramDroid–An approach towards malware detection in Android using opcode ngrams

Abstract

AbstractThe recent increase in Android's popularity has resulted in a swamp of attacks faced by the platform. Several researchers have come out with various static malware detection tools using opcodes as features since opcodes provide the details of intrinsic patterns of application raw data. This article presents a new malware detection approach CogramDroid based on opcode ngrams. The approach classifies the applications based on the relative frequency patterns of the opcode ngrams using the concept of word cooccurrence of natural language processing. The objective of the article is to develop a malware detection approach with high accuracy and time efficiency. The article also presents an analysis of the number of opcodes required for effective malware detection. In this study, an accuracy rate of 96.22% and an F1‐score of 96.69% is achieved using seven core opcodes and three grams.