Feature Analysis and Classification of Collusive Android App-Pairs Using DBSCAN Clustering Algorithm
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56396-6_6
Reference10 articles.
1. Bhandari, S., Laxmi, V., Zemmari, A., Gaur, M.S.: Intersection automata based model for android application collusion. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 901–908 (2016). https://doi.org/10.1109/AINA.2016.92
2. Blasco, J., Chen, T.M.: Automated generation of colluding apps for experimental research. J. Comput. Virol. Hacking Tech. 14, 127–138 (2018)
3. Bosu, A., Liu, F., Yao, D.D., Wang, G.: Collusive data leak and more: large-scale threat analysis of inter-app communications. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, ASIA CCS 2017, pp. 71–85. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3052973.3053004
4. Casolare, R., Giacomo, U.D., Martinelli, F., Mercaldo, F., Santone, A.: Android collusion detection by means of audio signal analysis with machine learning techniques. Procedia Comput. Sci. 192, 2340–2346 (2021). https://doi.org/10.1016/j.procs.2021.08.224. https://www.sciencedirect.com/science/article/pii/S1877050921017208, knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021
5. Advances in Intelligent Systems and Computing;R Casolare,2020
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