Malware Analysis Using Machine Learning
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-35501-1_28
Reference14 articles.
1. Ficco, M.: Malware analysis by combining multiple detectors and observation windows. IEEE Trans. Comput. 1 (2021). https://doi.org/10.1109/tc.2021.3082002
2. Souri, A., Hosseini, R.: A state-of-the-art survey of malware detection approaches using data mining techniques. Hum. Centric Comput. Inf. Sci. 8(3) (2018). https://doi.org/10.1186/s13673-018-0125-x
3. Roseline, S.A., Sasisri, A.D., Geetha, S., Balasubramanian, C.: Towards efficient malware detection and classification using multilayered random forest ensemble technique. In: 2019 International Carnahan Conference on Security Technology (ICCST) (2019). https://doi.org/10.1109/ccst.2019.8888406
4. Sethi, K., Kumar, R., Sethi, L., Bera, P., Patra, P.K.: A novel machine learning based malware detection and classification framework. In: 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) (2019). https://doi.org/10.1109/cybersecpods.2019.888
5. Kaur, G., Dhir, R., Singh, M.: Anatomy of ransomware malware: detection, analysis, and reporting. Int. J. Secur. Netw. 12(3), 188 (2017). https://doi.org/10.1504/ijsn.2017.084399
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