Optimized Static and Dynamic Android Malware Analysis Using Ensemble Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-31164-2_14
Reference21 articles.
1. Kural, O., Şahin, D., Akleylek, S., Kılıç, E., Ömüral, M.: Apk2Img4AndMal: Android Malware Detection Framework Based on Convolutional Neural Network. In: 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 731–734 (2021)
2. Mat, S., Razak, M., Kahar, M., Arif, J., Firdaus, A.: A Bayesian probability model for Android malware detection. ICT Express. (2021), https://www.sciencedirect.com/science/article/pii/S2405959521001235
3. Abdullah, T., Ali, W., Abdulghafor, R.: Empirical study on intelligent android malware detection based on supervised machine learning. Int. J. Adv. Comput. Sci. Appl. 11 (2020), https://dx.doi.org/10.14569/IJACSA.2020.0110429
4. Türker, S., Can, A.: AndMFC: Android Malware Family Classification Framework. In: 2019 IEEE 30th International Symposium on Personal, Indoor And Mobile Radio Communications (PIMRC Workshops). 1, 1–6 (2019)
5. Park, J., Chun, H., Jung, S.: API and permission-based classification system for Android malware analysis. In: 2018 International Conference on Information Networking (ICOIN), pp. 930–935 (2018)
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