<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine Learning (ML) techniques is compromised in the presence of irrelevant features and class imbalance. In this research, an efficient data pre-processing strategy was proposed to enhance the model’s generalizability. The class dissimilarity is addressed using k-Means SMOTE. After this, we furnish a hybrid feature selection method that combines filters and wrappers. Further, a hyperparameter-tuned Light Gradient Boosting Machine (LGBM) is analyzed by varying the optimal feature subsets. The experiments used the datasets – UNSW-NB15 and CICIDS-2017, yielding an accuracy of 90.71% and 99.98%, respectively. As the transparency and generalizability of the model depend significantly on understanding each component of the prediction, we employed the eXplainable Artificial Intelligence (XAI) method, SHapley Additive exPlanation (SHAP), to improve the comprehension of forecasted results.</p>
</abstract>

An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine Learning (ML) techniques is compromised in the presence of irrelevant features and class imbalance. In this research, an efficient data pre-processing strategy was proposed to enhance the model’s generalizability. The class dissimilarity is addressed using k-Means SMOTE. After this, we furnish a hybrid feature selection method that combines filters and wrappers. Further, a hyperparameter-tuned Light Gradient Boosting Machine (LGBM) is analyzed by varying the optimal feature subsets. The experiments used the datasets – UNSW-NB15 and CICIDS-2017, yielding an accuracy of 90.71% and 99.98%, respectively. As the transparency and generalizability of the model depend significantly on understanding each component of the prediction, we employed the eXplainable Artificial Intelligence (XAI) method, SHapley Additive exPlanation (SHAP), to improve the comprehension of forecasted results.

Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security

School of Computer Science and Engineering, VIT-AP University

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine...

Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security

Publicado en línea: 23 mar 2024

Páginas: 98 - 117

Recibido: 27 sept 2023

Aceptado: 14 dic 2023

DOI: https://doi.org/10.2478/cait-2024-0006

Palabras clave
Cyber security, Intrusion Detection System (IDS), Hybrid feature selection, SMOTE, Light Gradient Boosting Machine (LGBM)

© 2024 Seshu Bhavani Mallampati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Enhancing Intrusion Detection with Explainable AI: A Transparent Approach to Network Security

Publicado en línea: 23 mar 2024

Páginas: 98 - 117

Recibido: 27 sept 2023

Aceptado: 14 dic 2023

DOI: https://doi.org/10.2478/cait-2024-0006

Palabras claveCyber security, Intrusion Detection System (IDS), Hybrid feature selection, SMOTE, Light Gradient Boosting Machine (LGBM)

© 2024 Seshu Bhavani Mallampati et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Cyber security, Intrusion Detection System (IDS), Hybrid feature selection, SMOTE, Light Gradient Boosting Machine (LGBM)