Acta Informatica Pragensia 2022, 11(3), 348-360 | DOI: 10.18267/j.aip.199926

Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

Saad Ahmed Dheyab ORCID...1, Shaymaa Mohammed Abdulameer ORCID...2, Salama Mostafa ORCID...3
1 College of Engineering, University of Information Technology and Communications, Baghdad, Iraq
2 College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
3 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.

Keywords: Distributed Denial of Service (DDoS); Intrusion Detection Systems (IDS); Machine Learning (ML); Random Forest (RF); Decision Tree (DT); Dimensionality Reduction (DR).

Received: August 13, 2022; Revised: October 15, 2022; Accepted: November 11, 2022; Prepublished online: November 15, 2022; Published: December 26, 2022  Show citation

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Dheyab, S.A., Abdulameer, S.M., & Mostafa, S. (2022). Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction. Acta Informatica Pragensia, 11(3), 348-360. doi: 10.18267/j.aip.199
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    References

    1. Abbas, S. A., & Almhanna, M. S. (2021). Distributed Denial of Service Attacks Detection System by Machine Learning Based on Dimensionality Reduction. Journal of Physics: Conference Series, 1804(1), 012136. https://doi.org/10.1088/1742-6596/1804/1/012136 Go to original source...
    2. Abdulrahman, A. A., & Ibrahem, M. K. (2018). Evaluation of DDoS Attacks Detection in a CICIDS2017 Dataset Based on Classification Algorithms. Iraqi Journal of Information and Communications Technology, 1(3), 49-55. Go to original source...
    3. Alduailij, M., Khan, Q. W., Tahir, M., Sardaraz, M., Alduailij, M., & Malik, F. (2022). Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method. Symmetry, 14(6), 1095. https://doi.org/10.3390/sym14061095 Go to original source...
    4. Alharbi, Y., Alferaidi, A., Yadav, K., Dhiman, G., & Kautish, S. (2021). Denial-of-Service Attack Detection over IPv6 Network Based on KNN Algorithm. Wireless Communications and Mobile Computing, 2021, Article ID 8000869. https://doi.org/10.1155/2021/8000869 Go to original source...
    5. Arowolo, M. O., Adebiyi, M. O., Adebiyi, A. A., & Olugbara, O. (2021). Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier. Journal of Big Data, 8(1), 1-14. https://doi.org/10.1186/s40537-021-00415-z Go to original source...
    6. Ayesha, S., Hanif, M. K., & Talib, R. (2020). Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion, 59, 44-58. https://doi.org/10.1016/j.inffus.2020.01.005 Go to original source...
    7. Azizan, A. H., Mostafa, S. A., Mustapha, A., Foozy, C. F. M., Wahab, M. H. A., Mohammed, M. A., & Khalaf, B. A. (2021). A Machine Learning Approach for Improving the Performance of Network Intrusion Detection Systems. Annals of Emerging Technologies in Computing, 5(5), 201-208. https://doi.org/10.33166/aetic.2021.05.025 Go to original source...
    8. Bhaya, W., & EbadyManaa, M. (2017, March). DDoS attack detection approach using an efficient cluster analysis in large data scale. In 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT) (pp. 168-173). IEEE. https://doi.org/10.1109/NTICT.2017.7976110 Go to original source...
    9. Borkar, A., Donode, A., & Kumari, A. (2017, November). A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS). In 2017 International conference on inventive computing and informatics (ICICI) (pp. 949-953). IEEE. https://doi.org/10.1109/ICICI.2017.8365277 Go to original source...
    10. Disha, R. A., & Waheed, S. (2022). Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique. Cybersecurity, 5(1). https://doi.org/10.1186/s42400-021-00103-8 Go to original source...
    11. Elsayed, M. S., Le-Khac, N. A., Dev, S., & Jurcut, A. D. (2020, August). DDoSNet: A deep-learning model for detecting network attacks. In 2020 IEEE 21st International Symposium on" A World of Wireless, Mobile and Multimedia Networks" (WoWMoM) (pp. 391-396). IEEE. https://doi.org/10.1109/WoWMoM49955.2020.00072 Go to original source...
    12. Fazili, S., Grover, J., Wazir, S., & Mehta, I. (2021). Recent Trends in Dimension Reduction Methods. In Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development. EUDL. https://doi.org/10.4108/eai.27-2-2020.2303136 Go to original source...
    13. Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021). Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0. Electronics, 10(11), 1257. https://doi.org/10.3390/electronics10111257 Go to original source...
    14. Han, J., & Ge, Z. (2020). Effect of dimensionality reduction on stock selection with cluster analysis in different market situations. Expert Systems with Applications, 147, 113226. https://doi.org/10.1016/j.eswa.2020.113226 Go to original source...
    15. Hezam, A. A., Mostafa, S. A., Baharum, Z., Alanda, A., & Salikon, M. Z. (2021). Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks. JOIV: International Journal on Informatics Visualization, 5(4), 380-387. https://doi.org/10.30630/joiv.5.4.733 Go to original source...
    16. Khalaf, B. A., Mostafa, S. A., Mustapha, A., Mohammed, M. A., & Abduallah, W. M. (2019). Comprehensive Review of Artificial Intelligence and Statistical Approaches in Distributed Denial of Service Attack and Defense Methods. IEEE Access, 7, 51691-51713. https://doi.org/10.1109/ACCESS.2019.2908998 Go to original source...
    17. Khalaf, B. A., Mostafa, S. A., Mustapha, A., Mohammed, M. A., Mahmoud, M. A., Al-Rimy, B. A. S., Abd Razak, S., Elhoseny, M., & Marks, A. (2021). An Adaptive Protection of Flooding Attacks Model for Complex Network Environments. Security and Communication Networks, 2021, Article ID 5542919. https://doi.org/10.1155/2021/5542919 Go to original source...
    18. Kousar, H., Mulla, M. M., Shettar, P., & Narayan, D. G. (2021, June). Detection of DDoS Attacks in Software Defined Network using Decision Tree. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 783-788). IEEE. https://doi.org/10.1109/CSNT51715.2021.9509634 Go to original source...
    19. Li, M., Wang, H., Yang, L., Liang, Y., Shang, Z., & Wan, H. (2020). Fast hybrid dimensionality reduction method for classification based on feature selection and grouped feature extraction. Expert Systems with Applications, 150, 113277. https://doi.org/10.1016/j.eswa.2020.113277 Go to original source...
    20. Maranhão, J. P. A., da Costa, J. P. C. L., Javidi, E., de Andrade, C. A. B., & de Sousa, R. T. (2021). Tensor based framework for Distributed Denial of Service attack detection. Journal of Network and Computer Applications, 174, 102894. https://doi.org/10.1016/j.jnca.2020.102894 Go to original source...
    21. Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. M. (2021). Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset. IEEE Access, 9, 22351-22370. https://doi.org/10.1109/access.2021.3056614 Go to original source...
    22. Nagananda, N., & Savakis, A. (2021). GILDA++: Grassmann Incremental Linear Discriminant Analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4453-4461). https://openaccess.thecvf.com/content/CVPR2021W/DiffCVML/html/Nagananda_GILDA_Grassmann_Incremental_Linear_Discriminant_Analysis_CVPRW_2021_paper.html Go to original source...
    23. Novaes, M. P., Carvalho, L. F., Lloret, J., & Proença, M. L. (2020). Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment. IEEE Access, 8, 83765-83781. https://doi.org/10.1109/ACCESS.2020.2992044 Go to original source...
    24. Peng, X., Ma, Z., & Xu, H. (2020). Maximum Discriminant Difference Criterion for Dimensionality Reduction of Tensor Data. IEEE Access, 8, 193593-193607. https://doi.org/10.1109/access.2020.3032346 Go to original source...
    25. Priya, S. S., Sivaram, M., Yuvaraj, D., & Jayanthiladevi, A. (2020, March). Machine learning based DDoS detection. In 2020 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 234-237). IEEE. https://doi.org/10.1109/ESCI48226.2020.9167642 Go to original source...
    26. Rahal, B. M., Santos, A., & Nogueira, M. (2020). A Distributed Architecture for DDoS Prediction and Bot Detection. IEEE Access, 8, 159756-159772. https://doi.org/10.1109/access.2020.3020507 Go to original source...
    27. Rahman, O., Quraishi, M. A. G., & Lung, C. H. (2019, July). DDoS attacks detection and mitigation in SDN using machine learning. In 2019 IEEE world congress on services (SERVICES) (Vol. 2642, pp. 184-189). IEEE. https://doi.org/10.1109/SERVICES.2019.00051 Go to original source...
    28. Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of Dimensionality Reduction Techniques on Big Data. IEEE Access, 8, 54776-54788. https://doi.org/10.1109/ACCESS.2020.2980942 Go to original source...
    29. Saini, P. S., Behal, S., & Bhatia, S. (2020, March). Detection of DDoS attacks using machine learning algorithms. In 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 16-21). IEEE. https://doi.org/10.23919/INDIACom49435.2020.9083716 Go to original source...
    30. Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A. (2019, October). Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST) (pp. 1-8). IEEE. https://doi.org/10.1109/CCST.2019.8888419 Go to original source...
    31. Sharma, V., Verma, V., & Sharma, A. (2019, June). Detection of DDoS attacks using machine learning in cloud computing. In International Conference on Advanced Informatics for Computing Research (pp. 260-273). Springer, Singapore. https://doi.org/10.1007/978-981-15-0111-1_24 Go to original source...
    32. Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-To-Peer Networking and Applications, 12(2), 493-501. https://doi.org/10.1007/s12083-017-0630-0 Go to original source...
    33. Tuan, T. A., Long, H. V., Son, L. H., Kumar, R., Priyadarshini, I., & Son, N. T. K. (2020). Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 13(2), 283-294. https://doi.org/10.1007/s12065-019-00310-w Go to original source...
    34. Yungaicela-Naula, N. M., Vargas-Rosales, C., & Perez-Diaz, J. A. (2021). SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning. IEEE Access, 9, 108495-108512. https://doi.org/10.1109/ACCESS.2021.3101650 Go to original source...
    35. Zhang, B., Zhang, T., & Yu, Z. (2017, December). DDoS detection and prevention based on artificial intelligence techniques. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC) (pp. 1276-1280). IEEE. https://doi.org/10.1109/CompComm.2017.8322748 Go to original source...
    36. Zhijun, W., Qing, X., Jingjie, W., Meng, Y., & Liang, L. (2020). Low-Rate DDoS Attack Detection Based on Factorization Machine in Software Defined Network. IEEE Access, 8, 17404-17418. https://doi.org/10.1109/access.2020.2967478 Go to original source...
    37. Zong, Y., & Huang, G. (2019). A feature dimension reduction technology for predicting DDoS intrusion behavior in multimedia internet of things. Multimedia Tools and Applications, 80(15), 22671-22684. https://doi.org/10.1007/s11042-019-7591-7 Go to original source...

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