Abstract
Although great effort has been devoted to successfully detect Android malware, it still is a problem to be addressed. Its complexity increases due to the high number of features that can be obtained from Android apps in order to improve detection. Present paper proposes wrapper feature selection by applying a genetic algorithm and a Multilayer Perceptron. In order to validate this proposal, feature selection is performed on the well-known Drebin dataset on Apache Spark. Interesting results on the most informative features for the detection of existing Android malware have been obtained.
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González, S., Herrero, Á., Sedano, J., Corchado, E. (2020). Neuro-Evolutionary Feature Selection to Detect Android Malware. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_13
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