Publisher
Springer Nature Switzerland
Reference42 articles.
1. Hacktivists step back giving way to professionals: a look at DDos in Q3 2022 (2022). https://www.kaspersky.com/about/press-releases/2022_hacktivists-step-back-giving-way-to-professionals-a-look-at-ddos-in-q3-2022. Accessed 30 Jun 2023
2. Acar, A., Aksu, H., Uluagac, A., Conti, M.: A survey on homomorphic encryption schemes: Theory and implementation. ACM Comput. Surv. 51(4), 1–35 (2018)
3. Ali, T.E., Chong, Y.W., Manickam, S.: Machine learning techniques to detect a DDos attack in SDN: a systematic review. Appl. Sci. 13(5), 3183 (2023). https://www.mdpi.com/2076-3417/13/5/3183
4. Alom, M.Z., et al.: A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3), 292 (2019)
5. Amaizu, G., Nwakanma, C., Bhardwaj, S., Lee, J., Kim, D.: Composite and efficient DDos attack detection framework for b5g networks. Comput. Netw. 188, 107871 (2021). https://doi.org/10.1016/j.comnet.2021.107871