This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
T.N.V.S. Praveen,
Konakala Jyothi,
Patchigalla Bala Karthik,
Mudavath Balaji Naik2,
- Professor, Department of Computer Science and Engineering, Lakireddy-Bali-Reddy-College-of-Engineering, Mylavaram, NTR District, Andhra Pradesh, India
- Student, Department of Computer Science and Engineering, Lakireddy-Bali-Reddy-College-of-Engineering, Mylavaram, NTR District, Andhra Pradesh, India
- Student, Department of Computer Science and Engineering, Lakireddy-Bali-Reddy-College-of-Engineering, Mylavaram, NTR District, Andhra Pradesh, India
- Student, Department of Computer Science and Engineering, Lakireddy-Bali-Reddy-College-of-Engineering, Mylavaram, NTR District, Andhra Pradesh, India
Abstract
The ever-growing reliance on SDN-based services necessitates robust security measures against Distributed Denial-of-Service (DDoS) attacks that threaten service availability. This project investigates the development of a real-time prediction system for DDoS attacks in SDN environments, leveraging the power of machine learning. The proposed system employs a Decision Tree classification algorithm implemented in Python. To ensure accurate attack identification, the system meticulously addresses data preprocessing challenges inherent in network traffic datasets. These challenges include imbalanced class distributions, where normal traffic significantly outnumbers attack instances, and the presence of categorical features requiring transformation for machine learning algorithms. The system tackles these issues by employing techniques like oversampling to balance the class distribution and label encoding for categorical features. By effectively addressing these preprocessing hurdles, the model is empowered to analyze network traffic data and predict DDoS attacks with high accuracy. This real-time prediction capability can significantly enhance SDN security by enabling proactive mitigation strategies to safeguard service availability and prevent disruptions caused by DDoS attacks.
Keywords: Subcategory, DDoS attack prediction, machine learning, SDN security, network security, decision tree, classification algorithm, real-time prediction, threat detection, network traffic analysis, data preprocessing, imbalanced data, oversampling, categorical features, label encoding, python, machine learning framework
[This article belongs to Journal Of Network security (jons)]
T.N.V.S. Praveen, Konakala Jyothi, Patchigalla Bala Karthik, Mudavath Balaji Naik2. Real-time DDoS Attack Prediction in SDN Environments Using Machine Learning. Journal Of Network security. 2025; 13(01):16-27.
T.N.V.S. Praveen, Konakala Jyothi, Patchigalla Bala Karthik, Mudavath Balaji Naik2. Real-time DDoS Attack Prediction in SDN Environments Using Machine Learning. Journal Of Network security. 2025; 13(01):16-27. Available from: https://journals.stmjournals.com/jons/article=2025/view=196971
References
- Salman O, Elhajj I, Chehab A, Kayssi A. IoT survey: An SDN and fog computing perspective. Comput Netw. 2018 Oct 9; 143: 221–
- Dantas Silva FS, Silva E, Neto EP, Lemos M, Venancio Neto AJ, Esposito F. A taxonomy of DDoS attack mitigation approaches featured by SDN technologies in IoT scenarios. Sensors. 2020 May 29; 20(11):
- Dong S, Abbas K, Jain R. A survey on distributed denial of service (DDoS) attacks in SDN and cloud computing environments. IEEE Access. 2019 Jun 12; 7: 80813–28.
- Alashhab AA, Zahid MS, Azim MA, Daha MY, Isyaku B, Ali S. A survey of low rate ddos detection techniques based on machine learning in software-defined networks. Symmetry. 2022 Jul 29; 14(8):
- Banitalebi Dehkordi A, Soltanaghaei M, Boroujeni FZ. The DDoS attacks detection through machine learning and statistical methods in SDN. J Supercomput. 2021 Mar; 77(3): 2383–
- Batra R, Shrivastava VK, Goel AK. Anomaly Detection over SDN Using Machine Learning and Deep Learning for Securing Smart City. In Green Internet of Things for Smart Cities. CRC Press; 2021 Jun 28; 191–204.
- Alzahrani RJ, Alzahrani A. Security analysis of ddos attacks using machine learning algorithms in networks traffic. Electronics. 2021 Nov 25; 10(23):
- Woelfli W, Baltensperger W. On the change of latitude of Arctic East Siberia at the end of the Pleistocene. arXiv preprint arXiv:0704.2489. 2007 Apr 19.
- Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim KJ. A survey of deep learning-based network anomaly detection. Cluster Comput. 2019 Jan 16; 22: 949–
- He H, Ma Y, editors. Imbalanced learning: foundations, algorithms, and applications. New York City, US: Wiley-IEEE Press. 2013 Aug 9.
- Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst. 2009 Jun 1; 25(6): 599–
- Mirkovic J, Reiher P. A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Comput Commun Rev. 2004 Apr 1; 34(2): 39–53.
- Bonguet A, Bellaiche M. A survey of denial-of-service and distributed denial of service attacks and defenses in cloud computing. Future Internet. 2017 Aug 5; 9(3):
- Li C, Wu Y, Yuan X, Sun Z, Wang W, Li X, Gong L. Detection and defense of DDoS attack–based on deep learning in OpenFlow‐based SDN. Int J Commun Syst. 2018 Mar 25; 31(5):
Journal Of Network security
Volume | 13 |
Issue | 01 |
Received | 13/12/2024 |
Accepted | 22/01/2025 |
Published | 04/02/2025 |