Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method
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In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.
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Multiple techniques have been employed to uncover meaningful information from intrusion datasets, such as clustering, neural networks, association rules, decision trees, genetic algorithms, and nearest-neighbour methods.
“Multiple techniques have been employed to uncover meaningful information from intrusion datasets, such as clustering, neural networks, association rules, decision trees, genetic algorithms, and nearest-neighbour methods [136] evaluate the importance of features in IDS [77].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [136]
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- Authors
- Le, T.-T.-H., Kim, H., Kang, H., & Kim, H.
- Affiliations
- Hung Yen University of Technology and Education (VN)
- Pusan National University (KR)
- Corresponding author
- Kim, Howon
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-02-03
- Volume
- 22
- Issue
- 3
- Pages
- 1154
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8840013
- OpenAlex
- W4210659135
- Cited by
- 208
- References
- 63
- Citations by year
- 2026: 37 · 2025: 64 · 2024: 44 · 2023: 49 · 2022: 14
- FWCI
- 18.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- intrusion detection systems, Internet of Things security, decision tree ensembles, random forest, decision tree, SHAP, explainable AI, attack detection, Bot-IoT dataset, ToN-IoT, model interpretability
- MeSH terms
- Machine Learning, Humans, Computer Security, Neural Networks, Computer
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