ItemConference Paper

Analyzing the Role of LIME and SHAP in Explainable DoS Attack Detection for IoT Systems

Title
Analyzing the Role of LIME and SHAP in Explainable DoS Attack Detection for IoT Systems
Creator
Paul, Aditi; Kumari, Sweety; Navadia, Nipun R; Sinha, Somnath
Description
Explainable Artificial Intelligence (XAI) based tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are extensively used in various detection and prediction approaches. These tools extract feature importance from the datasets and explain the contribution of the features (feature importance) towards detection /prediction output both locally and globally. In the current study a performance analysis is represented on the behaviour of LIME and SHAP explainability towards Denial-of-Service Attack detection in Internet of Things. There are numerous Black-box models including Machine Learning which show high detection accuracies in such case but the output is not interpretable by the security analyst most of the time. this drawback is overcome by introducing LIME and SHAP interpretability to the output of BlackBox model by analysing feature importance of the attack dataset towards detection accuracy. However, LIME and SHAPE has different behaviour towards model-interpretability. SHAP is powerful in global explanation where LIME works efficiently on local interpretation. We have shown that these two different tools perform on same detection accuracies of DoS attack using Machine learning model. A random forest classifier is first selected with high detection accuracy on a simulated DoS attack dataset and at the output SHAP and LIME are executed for achieving both local and global explainability. The comparison shows how SHAP and LIME show strength and weakness in explaining model's behaviour both locally and globally. 2025 IEEE.
Source
4th International Conference on Automation, Computing and Renewable Systems, ICACRS 2025 - Proceedings;pp.428-434
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
DoS attack; feature importance; LIME and SHAP comparison; XAI based tools; XAI for machine learning
Coverage
Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Kumari S., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Navadia N.R., Pace University, New York City, NY, United States; Sinha S., CHRIST University, Department of Computer Science, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833154886-5;
Format
online
Language
English
Type
Conference paper

Citation

Paul, Aditi; Kumari, Sweety; Navadia, Nipun R; Sinha, Somnath, “Analyzing the Role of LIME and SHAP in Explainable DoS Attack Detection for IoT Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25894.

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