Abstract
Due to the exponential increase in the use of smart mobile devices, malware threats on those devices have been growing and posing security risks. To address this critical issue, we developed an Artificial Neural Network (ANN)-based malware detection system to detect unknown malware. In our system, we consider both permissions requested by applications and system calls associated with the execution of applications to distinguish between benign applications and malware. We used ANN, a representative machine learning technique, to understand the anomaly behavior of malware by learning the characteristic permissions and system calls used by applications. We then used the trained ANN to detect malware. Using real-world malware and benign applications, we conducted experiments on Android devices and evaluated the effectiveness of our developed system.
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Yu, W., Ge, L., Xu, G., Fu, X. (2014). Towards Neural Network Based Malware Detection on Android Mobile Devices. In: Pino, R., Kott, A., Shevenell, M. (eds) Cybersecurity Systems for Human Cognition Augmentation. Advances in Information Security, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-10374-7_7
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DOI: https://doi.org/10.1007/978-3-319-10374-7_7
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