Machine Learning-Based Detection of Distributed Denial-Of-Service Attacks in SDN
Keywords:
SDN, attacks, DDoS, Decision TreeAbstract
(SDN) is a technique for digitally building and designing hardware components. Dynamic changes can be made to the network connection settings. Because the link in the traditional network is fixed, dynamic change is not possible. Although SDN is a fantastic strategy, DDoS attacks are still possible. The DDoS attack is a danger to the internet. DDoS attacks can be mitigated by using the machine learning algorithm. DDoS attacks occur when multiple systems collaborate to target a single host at the same time. Devices in the infrastructure layer are managed by software from the control layer, which sits between the application and infrastructure layers in SDN. In this paper, we use a machine learning method called Decision Tree to detect malicious communications. Our results show that the Decision Tree determines whether the assault is safe or not.
References
- Dong, S., & Sarem, M. (2019). DDoS Attack Detection Method Based on Improved KNN with the Degree of DDoS Attack in Software-Defined Networks. IEEE Access, 8, 5039-5048.
- Dong, S., Abbas, K., & Jain, R. (2019). A survey on distributed denial of service (DDoS) attacks in SDN and cloud computing environments. IEEE Access, 7, 80813- 80828.
- Gu, Y., Li, K., Guo, Z., & Wang, Y. (2019). Semi supervised K-means DDoS detection method using hybrid feature selection algorithm. IEEE Access, 7, 64351- 64365.
- N. Meti, D. G. Narayan, and V. P. Baligar (2017, September). Machine learning algorithms are used in software defined networks to detect distributed denial of service attacks. In 2017, the International Conference on Advances in Computing, Communications, and Informatics (ICACCI) held its annual meeting in San Francisco (pp. 1366-1371). IEEE.
- IEEE DDoS Attack Identification and Defense Using SDN Based on Machine Learning Method, 15th International Symposium on Pervasive Systems, Algorithms, and Networks, 2018.
- 6.Muthamil Sudar, K., and P. Deepalakshmi (2020). A two-tiered security mechanism for detecting DDoS flooding attacks in software-defined networks that employs an entropy-based and C4. 5 technique. (Preprint), Journal of High Speed Networks, 1- 22.
- 7.Deepa, V., Sudar, K. M., and P. Deepalakshmi (2018, December). DDoS attack detection on the SDN control plane using Hybrid Machine Learning Techniques. The International Conference on Smart Systems and Innovative Technology (ICSSIT) was held in 2018. (pp. 299-303). IEEE.
- Deepa, V., Muthamil Sudar, K., and Deepalakshmi, P.
- Deepalakshmi. "Design of Ensemble Learning Methods for DDoS Detection in SDN Environment." 2019 International Conference on Vision for Emerging Communication and Networking Trends (ViTECoN). IEEE, 2019.\s9. "DDoS detection and defense mechanism based on cognitive-inspired computing in SDN,.
- "DDoS detection and defense mechanism based on cognitive-inspired computing in SDN,"
- "Time-based DDoS detection and mitigation for SDN controller," N. I. G.
- Time-based DDoS detection and mitigation for SDN controller," N. I. G
- A. Botta, W. de Donato, V. Persico, and A. Pescapé, Integration of cloud computing and the internet of things: A survey, Future Generation Computer Systems 56 (2016) 684-700.
- Han B., Gopalakrishnan V., Ji L., Lee S., Network function virtualization: Challenges and Opportunities for Innovation, IEEE Commun. Mag. 53 (2) (2015) 90-97. SDN OS, in: The Workshop on Hot Topics in Software Defined Networking, 2014, pp. 1–6.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.