Towards Explainable Meta-Learning for DDoS Detection
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866913412152295424 |
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| author | Zhou, Qianru Li, Rongzhen Xu, Lei Nallanathan, Arumugam Yang, Jian Fu, Anmin |
| author_facet | Zhou, Qianru Li, Rongzhen Xu, Lei Nallanathan, Arumugam Yang, Jian Fu, Anmin |
| contents | The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation Artificial Intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable Artificial Intelligence driven intrusion detection approach, based on artificial immune system. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_02255 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Towards Explainable Meta-Learning for DDoS Detection Zhou, Qianru Li, Rongzhen Xu, Lei Nallanathan, Arumugam Yang, Jian Fu, Anmin Artificial Intelligence Cryptography and Security Networking and Internet Architecture The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation Artificial Intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable Artificial Intelligence driven intrusion detection approach, based on artificial immune system. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic. |
| title | Towards Explainable Meta-Learning for DDoS Detection |
| topic | Artificial Intelligence Cryptography and Security Networking and Internet Architecture |
| url | https://arxiv.org/abs/2204.02255 |