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Improving Intrusion Detection Systems: Challenges with Public Datasets and the Role of Explainable AI: A Practical Guide Using NFS-2023-TE and HIKARI-2021
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Orientador(es)
Resumo(s)
Intrusion detection systems (IDS) based on public datasets often show promising results in academic
papers but fail to perform effectively in real-world scenarios due to flaws in dataset creation. This
discrepancy raises the question of whether explainable algorithms should be essential when building
machine learning-based intrusion detection systems. While recent research has highlighted some
deficiencies in these studies, new datasets have emerged, and practical guides on addressing these
issues are lacking. This thesis extends previous work by evaluating improvements in recent datasets and
providing new insights based on our findings. Our study reveals persistent issues in existing datasets and
demonstrates, through explainable AI techniques, why building intrusion detection systems with these
datasets should be approached with caution. By contributing guidelines on what to avoid when
developing an intrusion detection system, we also illustrate how certain aspects of the process require
deeper analysis before proposing new models. This research underscores the critical need for more
robust and representative datasets in IDS development, paving the way for more reliable and practical
cybersecurity solutions.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
Palavras-chave
intrusion detection explainable AI NIDS public dataset HIKARI-2021 NFS-2023-TE Cybersecurity SDG 8 - Decent work and economic growth