Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914140313878528 |
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| author | Yakubu, Paul Badu Santana, Lesther Rahouti, Mohamed Xin, Yufeng Chehri, Abdellah Aledhari, Mohammed |
| author_facet | Yakubu, Paul Badu Santana, Lesther Rahouti, Mohamed Xin, Yufeng Chehri, Abdellah Aledhari, Mohammed |
| contents | With the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and understanding of attack vectors. This paper presents an automated framework for detecting and interpreting DDoS attacks using machine learning (ML). The proposed method leverages the Tree-based Pipeline Optimization Tool (TPOT) to automate the selection and optimization of ML models and features, reducing the need for manual experimentation. SHapley Additive exPlanations (SHAP) is incorporated to enhance model interpretability, providing detailed insights into the contribution of individual features to the detection process. By combining TPOT's automated pipeline selection with SHAP interpretability, this approach improves the accuracy and transparency of DDoS detection. Experimental results demonstrate that key features such as mean backward packet length and minimum forward packet header length are critical in detecting DDoS attacks, offering a scalable and explainable cybersecurity solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems Yakubu, Paul Badu Santana, Lesther Rahouti, Mohamed Xin, Yufeng Chehri, Abdellah Aledhari, Mohammed Cryptography and Security Artificial Intelligence Machine Learning With the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and understanding of attack vectors. This paper presents an automated framework for detecting and interpreting DDoS attacks using machine learning (ML). The proposed method leverages the Tree-based Pipeline Optimization Tool (TPOT) to automate the selection and optimization of ML models and features, reducing the need for manual experimentation. SHapley Additive exPlanations (SHAP) is incorporated to enhance model interpretability, providing detailed insights into the contribution of individual features to the detection process. By combining TPOT's automated pipeline selection with SHAP interpretability, this approach improves the accuracy and transparency of DDoS detection. Experimental results demonstrate that key features such as mean backward packet length and minimum forward packet header length are critical in detecting DDoS attacks, offering a scalable and explainable cybersecurity solution. |
| title | Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.04114 |