Abstract
Smart devices are gradually becoming indispensable in people’s daily lives, and Android-based smart devices are taking over the main stream in mobile devices. However, while Android smart devices bring convenience to customers, they also bring problems. Due to the open-sourced nature of the Android system, malicious programs and software attacks pose a significant security risk to user data. Therefore, the detection of malware has always been a critical issue. For a long time, various malware detection schemes have been proposed, which have gradually improved the detection of malware. Traditional detection methods are based on static or dynamic detection techniques. In recent years, with the advancement of technology, malware detection based on machine learning ideas has been widely used, such as K-NN, deep learning, decision trees, and so on. Blockchain has been widely used in many fields since its birth. This paper combines traditional detection methods and ensemble learning algorithms to propose a malware detection technology based on QuorumChain framework (blockchain technology). The experimental results verify that the proposed new model is better than other models in precision, recall and f1-measure.
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Gao, F., Jiang, F., Zhang, Y., Doss, R. (2019). Quorum Chain-Based Malware Detection in Android Smart Devices. In: Doss, R., Piramuthu, S., Zhou, W. (eds) Future Network Systems and Security. FNSS 2019. Communications in Computer and Information Science, vol 1113. Springer, Cham. https://doi.org/10.1007/978-3-030-34353-8_16
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DOI: https://doi.org/10.1007/978-3-030-34353-8_16
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