Identification of Significant Permissions for Efficient Android Malware Detection

• URL: http://arxiv.org/abs/2103.00643v1
• Date: Sun, 28 Feb 2021 22:07:08 GMT
• Title: Identification of Significant Permissions for Efficient Android Malware Detection
• Authors: Hemant Rathore, Sanjay K. Sahay, Ritvik Rajvanshi, Mohit Sewak
• Abstract summary: One out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic-based malware detection systems are unable to cope up with current malware challenges. We propose an efficient Android malware detection system using machine learning and deep neural network.
• Score: 2.179313476241343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic-based malware detection systems are unable to cope up with current malware challenges and thus threaten the Android ecosystem. Therefore recently researchers have started exploring machine learning and deep learning based malware detection systems. In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network. We constructed a set of $16$ permissions ($8\%$ of the total set) derived from variance threshold, auto-encoders, and principal component analysis to build a malware detection engine that consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of $97.7\%$, which is better than the current state-of-art systems. We also observed that deep neural networks attain comparable accuracy to the baseline results but with a massive computational penalty.

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