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Machine Learning based Ensemble Technique for DDoS Attack Detection in Software-Defined Networking
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 6 |
| Year of Publication: 2023 |
| Authors: C. Srinivas, P.S. Avadhani, P. Prapoona Roja |
10.5120/ijca2023922712
|
C. Srinivas, P.S. Avadhani, P. Prapoona Roja . Machine Learning based Ensemble Technique for DDoS Attack Detection in Software-Defined Networking. International Journal of Computer Applications. 185, 6 ( May 2023), 22-25. DOI=10.5120/ijca2023922712
Abstract
Next-generation networks can benefit from a more dynamic and successfully controlled network design because of a new network paradigm termed the Software-Defined Network (SDN). Network administrators may simply monitor and manage the entire network using the design of the customizable centralized controller. A number of attack vectors simultaneously target it because of its centralized nature. DDoS attacks are the most efficient type of attack against the SDN. The goal of this work is to classify SDN flow as either normal or assault traffic using ML techniques a public "DDoS attack SDN Dataset" with 23 characteristics in total. The dataset comprises both legitimate and malicious traffic for the TCP, UDP, and ICMP (TCP). The dataset, which includes over 100,000 recordings, offers statistical statistics such byte count, time sec, packet rate, and packet per flow, with the exclusion of characteristics that define source and target devices. In this paper DDoS attack was detected using Various ML Algorithms such as K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms The experimental results demonstrate that an Ensemble Random Forest algorithm was given 99.99% classification accuracy than the other methods
References
- Nam, T.M.; Phong, P.H.; Khoa, T.D.; Huong, T.T.; Nam, P.N.; Thanh, N.H.; Thang, L.X.; Tuan, P.A.; Dung, L.Q.; Loi, V.D. Self-organizing map-based approaches in DDoS flooding detection using SDN. In Proceedings of the 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, 10–12 January 2018; pp. 249–254.
- Detection of myocardial infarction from ECG signals. In Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, 2–5 May 2018; pp. 1–4.
- Karan, B.V.; Narayan, D.G.; Hiremath, P.S. Detection of DDoS Attacks in Software Defined Networks. In Proceedings of the 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 20–22 December 2018; pp. 265–270.
- Deepa, V.; Sudar, K.M.; Deepalakshmi, P. Design of Ensemble Learning Methods for DDoS Detection in SDN Environment. In Proceedings of the International Conference on Vision towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India, 30–31 March 2019
- Hosseini, S.; Azizi, M. The hybrid technique for DDoS detection with supervised learning algorithms. Comput. Netw. 2019, 158, 35–45.
- Shao, E. Encoding IP Address as a Feature for Network Intrusion Detection. Ph.D. Dissertation, Purdue University Graduate School, West Lafayette, Indiana, 2019.
- Adhikary, K.; Bhushan, S.; Kumar, S.; Dutta, K. Decision Tree and Neural Network Based Hybrid Algorithm for Detecting and Preventing DDoS Attacks in VANETS. Int. J. Innov. Technol. Explor. Eng. 2020, 9, 669–675
- Ujjan, R.M.A.; Pervez, Z.; Dahal, K.; Bashir, A.K.; Mumtaz, R.; González, J. Towards slow and adaptive polling sampling for deep learning-based DDoS detection in SDN. Future. Gener. Comput. Syst. 2020, 111, 763–779.
- Ravi, N.; Shalinie, S.M. Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture. IEEE Internet Things J. 2020, 7, 3559–3570.
- Damasevicius, R.; Venckauskas, A.; Grigaliunas, S.; Toldinas, J.; Morkevicius, N.; Aleliunas, T.; Smuikys, P. Litnet-2020: An annotated real-world network flow dataset for network intrusion detection. Electronics 2020, 9, 800.
- Erhan, D.; Anarım, E. Bo ̆gaziçi] University distributed denial of service dataset. Data Brief 2020, 32, 106187.
- Elsayed, M.S.; Le-Khac, N.A.; Jurcut, A.D. InSDN: A novel SDN intrusion dataset. IEEE Access 2020, 8, 165263–165284.
- Ahuja, N.; Singal, G.; Mukhopadhyay, D. “DDOS attack SDN Dataset”, Mendeley Data, V1; Bennett University: Greater Noida, India, 2020.
- Yong, B.; Wei, W.; Li, K.C.; Shen, J.; Zhou, Q.; Wozniak, M.; Połap, D.; Damasevicius, R. Ensemble machine learning approaches for web shell detection in Internet of things environments. Trans. Emerg. Telecomm. Technol. 2020, e4085.
- Ahuja, N.; Singal, G.; Mukhopadhyay, D. DLSDN: Deep learning for DDOS attack detection in software defined networking. In Proceedings of the 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 28–29 January 2021; pp. 683–688.
- Cil, A.E.; Yildiz, K.; Buldu, A. Detection of DDoS attacks with feed forward based deep neural network model. Expert Syst. Appl.2021, 169, 114520
- Kushwah, G.S.; Ranga, V. Optimized extreme learning machine for detecting DDoS attacks in cloud computing. Comput. Secur.2021, 105, 102260.
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