ARCHIVES

1. Kumar, Sachin, Prayag Tiwari, and Mikhail Zymbler. "Internet of Things is a revolutionary approach for future technology enhancement:
a review." Journal of Big data 6.1 (2019): 1-21.
2. Snehi, Manish, and Abhinav Bhandari. "Vulnerability retrospection of security solutions for software-defined Cyber–Physical System
against DDoS and IoT-DDoS attacks." Computer Science Review 40 (2021): 100371.
3. Chang, Rocky KC. "Defending against flooding-based distributed denial-of-service attacks: A tutorial." IEEE communications magazine
40.10 (2002): 42-51.
4. Divyang Dave, Meet Kava, R. K. Gupta and Kaushal Shah, “Deep Learning approach for Intrusion Detection System, IEEE International
Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES), 2022. Doi:
10.1109/TRIBES52498.2021.9751643.
5. R. K. Gupta et al., “An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large Scale
5G”, Wireless Communications and Mobile Computing 2022.
6. Khuphiran, Panida, et al. "Performance comparison of machine learning models for ddos attacks detection." 2018 22nd International
Computer Science and Engineering Conference (ICSEC). IEEE, 2018.
7. Farnaaz, Nabila, and M. A. Jabbar. "Random forest modeling for network intrusion detection system." Procedia Computer Science 89
(2016): 213-217.
8. Li, Yang, and Li Guo. "An active learning based TCM-KNN algorithm for supervised network intrusion detection." Computers & security
26.7-8 (2007): 459-467.
9. Panda, Mrutyunjaya, and Manas Ranjan Patra. "Network intrusion detection using naive bayes." International journal of computer
science and network security 7.12 (2007): 258-263.
10. Yong, Li, and Zhang Bo. "An intrusion detection model based on multi-scale CNN." 2019 IEEE 3rd Information Technology, Networking,
Electronic and Automation Control Conference (ITNEC). IEEE, 2019.
11. Vu, N.H. DDoS attack detection using K-Nearest Neighbor classifier method. In Proceedings of the International Conference on
Telehealth/Assistive Technologies, Baltimore, Maryland, USA, 16–18 April 2008; IEEE: Piscataway Township, NJ, USA, 2008; pp. 248–
253.
12. Cheng, J.; Yin, J.; Liu, Y.; Cai, Z.;Wu, C. DDoS attack detection using IP address feature interaction. In Proceedings of the IEEE
International Conference on Intelligent Networking and Collaborative Systems, Thessalonika, Greece, 24–26 November 2010; IEEE:
Piscataway Township, NJ, USA, 2009; pp. 113–118.
13. Wang, C.; Zheng, J.; Li, X. Research on DDoS attacks detection based on RDF-SVM. In Proceedings of the 10th International Conference
on Intelligent Computation Technology and Automation, Changsha, China, 9–12 October 2017.
14. Fadlil, A.; Riadi, I.; Aji, S. Review of detection DDoS attack detection using Naïve Bayes classifier for network forensics. Bull. Electr.
Eng. Inform. 2017, 6, 140–148. [CrossRef]
15. Dincalp, U. Anomaly based distributed denial of service attack detection and prevention with machine learning. In Proceedings of the
2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, Ankara, Turkey, 19–21 October 2018.
16. Ahanger, T.A. An effective approach of detecting DDoS using artificial neural networks. In Proceedings of the 2017 International
Conference onWireless Communications, Signal Processing and Networking, Chennai, India, 22–24 March 2017; IEEE: Piscataway
Township, NJ, USA, 2017; pp. 707–711.
17. Zahid Hasan, Md., Zubair Hasan, K. M., & Sattar, Abdus (2018). Burst header packet flood detection in optical burst switching network
using deep learning model. Procedia Computer Science, 143, 970–977.
18. Krishnan, Prabhakar, Duttagupta, Subhasri, & Achuthan, Krishnashree (2019). VARMAN: Multi-plane security framework for software
defined networks. Computer Communications, 148, 215–239.