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Performance optimization in ddos prediction with ensemble based approach

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Abstract

Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach for DDoS attack detection, leveraging the strengths of three different classifiers: Adaboost, K-Nearest Neighbors (KNN), and Random Forest. We apply data normalization during pre-processing, utilize a Multi-Layer Perceptron (MLP) for feature extraction, and combine the classifiers using an ensemble approach. The performance of each individual classifier and the ensemble is extensively evaluated, and the results demonstrate the effectiveness of the proposed ensemble in accurately identifying and mitigating DDoS attacks.

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References

  1. Chavan N, Kukreja M, Jagwani G, Nishad N, Deb N (2022) DDoS attack detection and botnet prevention using machine learning. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 1159–1163. https://doi.org/10.1109/ICACCS54159.2022.9785247

  2. Luong T-K, Tran T-D, Le G-T (2020) DDoS attack detection and defense in SDN based on machine learning. In 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), pp 31–35. https://doi.org/10.1109/NICS51282.2020.9335867

  3. Sumantra I, Indira Gandhi S (2020) DDoS attack Detection and Mitigation in Software Defined Networks. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp 1–5. https://doi.org/10.1109/ICSCAN49426.2020.9262408

  4. Li J, Liu Y, Gu L (2010) DDoS attack detection based on neural network. In 2010 2nd International Symposium on Aware Computing, pp 196–199. https://doi.org/10.1109/ISAC.2010.5670479

  5. Kousar H, Mulla MM, Shettar P, G ND (2021) DDoS Attack Detection System using Apache Spark. In 2021 International Conference on Computer Communication and Informatics (ICCCI), pp 1–5. https://doi.org/10.1109/ICCCI50826.2021.9457012

  6. Yang K, Zhang J, Xu Y, Chao J (2020) DDoS Attacks Detection with AutoEncoder. In NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, pp 1–9. https://doi.org/10.1109/NOMS47738.2020.9110372

  7. Agarwal A, Singh R, Khari M (2022) Detection of DDOS Attack Using IDS Mechanism: A Review. In 2022 1st International Conference on Informatics (ICI), pp 36–46. https://doi.org/10.1109/ICI53355.2022.9786899

  8. Meenakshi, Kumar K, Behal S (2021) Distributed denial of service attack detection using deep learning approaches. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), pp 491–495

  9. Zhang Y, Wei S, Zhang L, Liu C (2019) Comparing the performance of random forest, SVM and their variants for ECG quality assessment combined with nonlinear features. J Med Biol Eng 39(3):381–392. https://doi.org/10.1007/s40846-018-0411-0

    Article  Google Scholar 

  10. Vanitha KS, UMA SV, Mahidhar SK (2017) Distributed denial of service: Attack techniques and mitigation. In 2017 International Conference on Circuits, Controls, and Communications (CCUBE), pp. 226–231. https://doi.org/10.1109/CCUBE.2017.8394146

  11. Abdul Rehman Javed (2020) (PDF) Ensemble Adaboost classifier for accurate and fast detection of botnet attacks in connected vehicles. RsearchGate, Accessed: Dec. 23, 2023. [Online]. Available: https://www.researchgate.net/publication/343661977_Ensemble_Adaboost_classifier_for_accurate_and_fast_detection_of_botnet_attacks_in_connected_vehicles

  12. Ashraf A, Elmedany WM (2021) IoT DDoS attacks detection using machine learning techniques: A Review. In 2021 International Conference on Data Analytics for Business and Industry (ICDABI), pp 178–185. https://doi.org/10.1109/ICDABI53623.2021.9655789

  13. Zhang W et al (2023) Feature importance measure of a multilayer perceptron based on the presingle-connection layer. Knowl Inf Syst. https://doi.org/10.1007/s10115-023-01959-7

    Article  Google Scholar 

  14. Yeom S, Choi C, Kim K (2022) LSTM-Based Collaborative Source-Side DDoS Attack Detection. IEEE Access 10:44033–44045. https://doi.org/10.1109/ACCESS.2022.3169616

    Article  Google Scholar 

  15. Liu S, Zhang K, Chen X (2020) A feature selection algorithm for multilayer perceptron based on simultaneous two-sample representation. In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), pp 270–275. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00059

  16. Najar AA, Manohar Naik S (2022) DDoS attack detection using MLP and Random Forest Algorithms. Int J Inf Technol 14(5):2317–2327. https://doi.org/10.1007/s41870-022-01003-x

    Article  Google Scholar 

  17. Banitalebi Dehkordi A, Soltanaghaei M, Boroujeni FZ (2021) The DDoS attacks detection through machine learning and statistical methods in SDN. J Supercomput 77(3):2383–2415. https://doi.org/10.1007/s11227-020-03323-w

    Article  Google Scholar 

  18. Lv D, Cheng X, Zhang J, Zhang W, Zhao W, Xu H (2022) DDoS Attack detection based on CNN and federated learning. In 2021 Ninth International Conference on Advanced Cloud and Big Data (CBD), pp 236–241. https://doi.org/10.1109/CBD54617.2021.00048

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SoET, BGSB University, Rajouri, India

    Amit Dogra

  2. Department of Computer Science Engineering and Technology, Guru Nanak Dev University, Regional Campus, Gurdaspur, India

    Taqdir

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  1. Amit Dogra
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  2. Taqdir
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Correspondence to Amit Dogra.

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Dogra, A., Taqdir Performance optimization in ddos prediction with ensemble based approach. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18940-3

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