doi: 10.18178/ijml.2023.13.4.1147
Detection of DDoS Attacks Using SHAP-Based Feature Reduction
C Cynthia1,*
,
Debayani Ghosh2 ,
Gopal Krishna Kamath1
- 1Department of Electrical and Electronics Engineering at BITS-Pilani Hyderabad Campus, Hyderabad, Telangana, India.
- 2Department of Electronics and Communication Engineering at Thapar Institute of Engineering and Technology, Patiala, Punjab, India.
- Manuscript receivedFebruary 10, 2023
- revisedMarch 13, 2023
- acceptedApril 8, 2023
Abstract
Machine learning techniques are widely used to protect cyberspace against malicious attacks. In this paper, we propose a machine learning-based intrusion detection system to alleviate Distributed Denial-of-Service (DDoS) attacks, which is one of the most prevalent attacks that disrupt the normal traffic of the targeted network. The model prediction is interpreted using the SHapley Additive exPlanations (SHAP) technique, which also provides the most essential features with the highest Shapley values. For the proposed model, the CICIDS2017 dataset from Kaggle is used for training the classification algorithms. The top features selected by the SHAP technique are used for training a Conditional Tabular Generative Adversarial Networks (CTGAN) for synthetic data generation. The CTGAN-generated data are then used to train prediction models such as Support Vector Classifier (SVC), Random Forest (RF), and Naïve Bayes (NB). The performance of the model is characterized using a confusion matrix. The experiment results prove that the attack detection rate is significantly improved after applying the SHAP feature selection technique.
Keywords
- DDoS
- SHAP
- IDS
- machine learning
- CTGAN
How to Cite
C Cynthia, Debayani Ghosh, and Gopal Krishna Kamath, "Detection of DDoS Attacks Using SHAP-Based Feature Reduction," International Journal of Machine Learning, vol. 13, no. 4, pp. 173-180, 2023. https://doi.org/10.18178/ijml.2023.13.4.1147
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).