Detection and Prevention of DDoS Attacks Using Machine Learning Algorithms on Computer Networks
DOI:
https://doi.org/10.30742/ijremte.v3i1.46Keywords:
DDoS, network security, machine learning, attack detection, attack preventionAbstract
The dynamic and unpredictable nature of Distributed Denial of Service (DDoS) attacks continues to pose a critical threat to the availability of computer network services. Traditional security measures, including static firewalls and signature-based intrusion detection systems, have proven inadequate for identifying novel attack variants whose traffic characteristics closely mimic legitimate network activity. To address this challenge, the present study introduces an automated framework for DDoS detection and mitigation, which is built upon machine learning algorithms. Four distinct algorithms were benchmarked in this study: Decision Tree, Random Forest, Support Vector Machine (SVM), and XGBoost. Among these, Random Forest delivered the most superior performance. When evaluated on a 20% hold-out test subset, this optimal model achieved an accuracy of 98.80%, precision of 98.51%, recall of 99.10%, and an F1-score of 98.80%. The exceptionally high recall rate confirms the model's effectiveness in capturing nearly all malicious traffic while maintaining an extremely low false-negative rate, whereas the high precision indicates a minimal occurrence of false alarms. The primary novelty of this work rests on the seamless integration of accurate machine-learning-based detection with empirically quantifiable automated mitigation responses—a holistic approach that remains rarely validated experimentally within a single cohesive framework. Consequently, this integrated strategy offers a highly adaptive and dependable solution for reinforcing network security and ensuring sustained service availability against the ever-evolving landscape of DDoS attacks.
References
. Stallings, W. (2020). Network Security Essentials: Applications and Standards (7th ed.). Pearson.
. Kurose, J. F., & Ross, K. W. (2022). Computer Networking: A Top-Down Approach (8th ed.). Pearson.
. Mirkovic, J., & Reiher, P. (2004), A Taxonomy of DDoS Attack and DDoS Defense Mechanisms, ACM SIGCOMM Computer Communication Review, 34(2), 39–53.
. Zargar, S. T., Joshi, J., & Tipper, D. (2013), A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks, IEEE Communications Surveys & Tutorials, 15(4), 2046–2069.
. Zargar, S. T., Joshi, J., & Tipper, D. (2013), A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks, IEEE Communications Surveys & Tutorials, 15(4), 2046–2069.
. Sommer, R., & Paxson, V. (2010), Outside the Closed World: On Using Machine Learning for Network Intrusion Detection, IEEE Symposium on Security and Privacy.
. Buczak, A. L., & Guven, E. (2016), A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection, IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
. Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2019), Applying Deep Learning Approaches for Network Traffic Prediction, Intrusion Detection, and Future Internet Security, Book Chapter IGI Global.
. Buczak, A. L., & Guven, E. (2016), A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection, IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
. Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020),Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study, Journal of Information Security and Applications, 50, 102419.
. Stallings, W. (2020), Data and Computer Communications (11th ed.), Publisher :Pearson.
. Stallings, W., & Brown, L. (2018), Computer Security: Principles and Practice (4th ed.), Publisher :Pearson.
. Bishop, M. (2019), Computer Security: Art and Science (2nd ed.), Publisher :Addison-Wesley.
. Axelsson, S. (2000), Intrusion Detection Systems: A Survey and Taxonomy, Technical Report : Chalmers University of Technology.
. Scarfone, K., & Mell, P. (2007), Guide to Intrusion Detection and Prevention Systems (IDPS), Publisher :National Institute of Standards and Technology (NIST), Special Publication 800-94.
. Mitchell, T. M. (1997), Machine Learning, Publisher : McGraw-Hill.
. Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Publisher : Springer.
. Buczak, A. L., & Guven, E. (2016), A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection, Journal : IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020), Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study,Journal : Journal of Information Security and Applications, 50, 102419.
. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018), Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization (CICIDS2017), Conference : Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP).
. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009), A Detailed Analysis of the KDD CUP 99 Data Set, Conference : IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).
. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
