Manuscript received February 10, 2023; revised March 13, 2023; accepted April 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.
Index Terms—DDoS, SHAP, IDS, machine learning,
CTGAN
C Cynthia and Gopal Krishna Kamath are with the Department of
Electrical and Electronics Engineering at BITS-Pilani Hyderabad Campus,
Hyderabad, Telangana, India. Email:
gopal.kamath@hyderabad.bits-pilani.ac.in (G.K.K.)
Debayani Ghosh is with the Department of Electronics and
Communication Engineering at Thapar Institute of Engineering and
Technology, Patiala, Punjab, India. Email: debayani.ghosh@thapar.edu
(D.G.)
*Correspondence: p20210415@hyderabad.bits-pilani.ac.in (C.C.)
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.
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).