[This article belongs to Volume - 54, Issue - 02]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-26-07-2022-236

Title : Android Malware Detection Using Genetic Algorithm And Machine Learning
V. Nayva sree, Rayannagari Keertana Reddy, Yellamshetty Preethika, P.Chandrika Reddy

Abstract :

Android was the most popular mobile operating system amongst smart phone users. Its high popularity, combined with the extended use of smart phones for everyday tasks as well as storing or accessing sensitive and personal data, has made Android applications the target of numerous malware attacks over the last few years and in the present. In this paper based on the relevant features from the set of permission by combining genetic algorithm and simulated annealing, and three algorithms GASA-SVM, GASADT, and GASA-KNN are developed based on this approach. The Drebin dataset with feature selecting actives are used to compare the malware accuracy. The system improves Android malware detection accuracy, and the GASA-SVM with the best value of 0.9707 has the best result.