Abstract
In these years, deep learning methods are used to detect Android malware due to low efficient manual checking and increase in Android malware. Data set quality defines the performance of these models. Low quality of training data set will result in reduced performance. Manual checking guarantees the data set quality in real world. Trained model may cause failure by malicious applications in Google Play. Artificial bee colony (ABC) algorithm based on selective swarm intelligence technique is proposed to rectify this issue, and it is robust detection technique of Android malware. The data set quality will not affect the effectiveness of solution. Better component learner’s combination can be computed by genetic algorithm in proposed model, which will enhance the robustness of model. In same area, better robust performance is exhibited by the proposed technique than other methods.
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References
Qing, S.H.: Research progress on Android security. Ruan Jian XueBao/J. Softw. 27(1), 45–71 (2016). http://www.jos.org.cn/1000-9825/4914.html. (in Chinese)
Zhang, R., Yang, J.Y.: Android malware detection based on permission relevance. J. Comput. Appl. 34(5), 1322–1325 (2014)
Yang, H., Zhang, Y., Hu, Y., et al.: Android malware detection method based on permission sequential pattern mining algorithm. Transactions 2013(S1), 106–115 (2013)
Li, W., Ge, J., Dai, G.: detecting malware for android platform: an SVM-based approach. In: IEEE International Conference on Cyber Security and Cloud Computing, pp. 464–469. IEEE Press, Piscataway, New Jersey, USA (2015)
Zhao, Y., Hu, L., Xiong, H., et al.: Dynamic analysis scheme of Android malware based on Sandbox. Netinfo Secur. 12, 21–26 (2014)
Enck, W., Gilbert, P., Han, S., et al.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. In: ACM Transactions on Computer Systems, pp. 393–407. ACM Press, New York, NY, USA (2014)
Runkang, S., Guojun, P., Jingwen, L., et al.: Behavior oriented method of Android malware detection and its effectiveness. J. Comput. Appl. 36(4), 973–978 (2016)
Yang, H., Zhang, Y., Hu, Y., et al.: A malware behavior detection system of Android application based on multi-class features. Chin. J. Comput. 37(1), 15–27 (2014)
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Sharma, A., Kumar, M. (2021). Feature Selection and Classification Based on Swarm Intelligence Approach to Detect Malware in Android Platform. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K., Suryani, E. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-15-5345-5_9
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DOI: https://doi.org/10.1007/978-981-15-5345-5_9
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