Abstract
Android operating systems based mobile phones are common in nowadays due to its ease of use and openness. Hundreds of Android based mobile applications are uploaded in the internet every day, which can be benign or malicious. The increase in the growth of malicious Android applications is alarming. Hence advanced solutions for the detection of malware is needed. In this paper, a novel malware detection framework is proposed that uses integrated static features and Support Vector Machine (SVM) classifier. The static features considered include permissions, API calls and opcodes. Out of these features, most significant ones are selected using Pearson correlation coefficient and N-grams. Each of these features are then integrated and fed to a classifier. The experimental evaluation of the proposed method and comparison with existing methods shows that the proposed framework is better.
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Ajeena Beegom, A.S., Ashok, G. (2020). Malware Detection in Android Applications Using Integrated Static Features. In: Thampi, S., Martinez Perez, G., Ko, R., Rawat, D. (eds) Security in Computing and Communications. SSCC 2019. Communications in Computer and Information Science, vol 1208. Springer, Singapore. https://doi.org/10.1007/978-981-15-4825-3_1
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DOI: https://doi.org/10.1007/978-981-15-4825-3_1
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