GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking

Abstract

With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work. However, malware such as viruses, Trojans, and extortion applications have introduced threats to personal privacy and social security. Malware of the Android operating system has a great variety and updates rapidly. Android malware detection is faced with the problems of high feature dimension and unsatisfied detection accuracy of single classification algorithms. In this work, an Android malware detection framework GA-StackingMD is presented, which employs Stacking to compose five different base classifiers, and Genetic Algorithm is applied to optimize the hyperparameters of the framework. Experiments show that Stacking could effectively improve malware detection accuracy compared with single classifiers. The presented GA-StackingMD achieves 98.43% and 98.66% accuracies on CIC-AndMal2017 and CICMalDroid2020 data sets, which shows the effectiveness and feasibility of the proposed method.

Keywords

• Android malware detection
• integrated machine learning
• stacking
• genetic algorithm