Mobile malware detection with imbalanced data using a novel synthetic oversampling strategy and deep learning

Khoda, Mahbub; Kamruzzaman, Joarder; Gondal, Iqbal; Imam, Tasadduq; Rahman, Ashfaqur

Title: Mobile malware detection with imbalanced data using a novel synthetic oversampling strategy and deep learning
Creator: Khoda, Mahbub; Kamruzzaman, Joarder; Gondal, Iqbal; Imam, Tasadduq; Rahman, Ashfaqur
Date: 2020
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/178772
Identifier: vital:15472
Identifier: ISBN:978-1-7281-9722-7
Identifier: https://doi.org/10.1109/WiMob50308.2020.9253433
Abstract: Mobile malware detection is inherently an imbalanced data problem since the number of benign applications in the market is far greater than the number of malicious applications. Existing methods to handle imbalanced data, such as synthetic minority over-sampling, do not translate well into this domain since mobile malware detection generally deals with binary features and these methods are designed for continuous features. Also, methods adapted for categorical features cannot be applied here since random modifications of features can result in invalid sample generation. In this work, we propose a novel technique for generating synthetic samples for mobile malware detection with imbalanced data. Our proposed method adds new data points in the sample space by generating synthetic malware samples which also preserves the original functionality of the malicious apps. Experiments show that the proposed approach outperforms existing techniques in terms of precision, recall, F1score, and AUC. This study will be useful in building deep neural network-based systems to handle imbalanced data for mobile malware detection. © 2020 IEEE.
Relation: 16th International Conference on Wireless and Mobile Computing, Networking and Communications (IEEE WiMob), Virtual, Thessaloniki, 12 to 14 October 2020, International Conference on Wireless and Mobile Computing, Networking and Communications
Rights: All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Subject: Deep neural networks; Malware; Mobile computing; Binary features; Categorical features; Continuous features; Imbalanced data; Imbalanced data problems; Network based systems; Novel techniques; Sample generations
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