GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Malware Detection Techniques · Network Security and Intrusion Detection · IoT-based Smart Home Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
Jilin Province Key Laboratory of Network and Information Security, Changchun 130022, China
Information Center, Changchun University of Science and Technology, Changchun 130022, China
Funding
Science and Technology Research Project of the Education Department of Jilin Province · Opening Project of Guangdong Province Key Laboratory of Information Security Technology
References (46)
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