Author:
Aggarwal Kapil,Yadav Santosh Kumar
Publisher
Springer Nature Singapore
Reference30 articles.
1. Vivekanandam, B. (2021). Design an adaptive hybrid approach for genetic algorithm to detect effective malware detection in android division. Journal of Ubiquitous Computing and Communication Technologies, 3(2), 135–149.
2. Jose, R. R., & Salim, A. (2019). Integrated static analysis for malware variants detection. International Conference on Inventive Computation Technologies (pp. 622–629). Cham: Springer.
3. Kumar, A. A., Anoosh, G. P., Abhishek, M. S., & Shraddha, C. (2020). An effective machine learning-based file malware detection—a survey. In International Conference on Communication, Computing and Electronics Systems (pp. 355–360). Springer, Singapore.
4. Deshotels, L., Notani, V., & Lakhotia, A. (2014). Droidlegacy: Automated familial classification of android malware. In Proceedings of ACM SIGPLAN on Program Protection and Reverse Engineering Workshop 2014 (p. 3). ACM.
5. Yerima, S. Y., Sezer, S., & McWilliams, G. (2014). Analysis of Bayesian classification-based approaches for Android malware detection. IET Information Security, 8(1), 25–36.