Merge pull request #254 from ibalampanis/master

Add seminar for 2023-12-13

content/seminars/2023-12-13.md

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+title: A systematic review of datasets for intrusion detection systems
+date: 2023-12-13
+presenter: Ilias Balampanis
+category: seminars
+
+Intrusion Detection Systems (IDS) based on Machine Learning (ML)
+techniques are essential for cybersecurity, utilizing datasets to
+detect and mitigate malicious network or system activities.
+The efficacy of IDSs is contingent on datasets that are both
+extensive and representative of actual cyber threats, ensuring accurate
+and robust system performance evaluation. This paper reports on a
+systematic literature review (SLR) that examines the landscape of
+datasets for IDS training and evaluation. The SLR explores the traits,
+creation methods, and constraints of these datasets, alongside the
+identification of challenges in their generation and utility. Highlighted
+is the variance in dataset quality, the gradual pace of development,
+and the ongoing deficit of datasets in the intrusion detection domain.
+With the continuous evolution of cyber threats, a persistent reassessment
+of these datasets is imperative for maintaining their pertinence and
+efficacy. Our SLR aggregates and dissects the extant research to elucidate
+the strengths and weaknesses of current datasets, determining their aptness
+for varied IDS contexts. The objective is to delineate the present state of
+IDS datasets, suggest measures for their enhancement, and propose future
+research directions. We emphasize the need for standardized, comprehensive,
+and ethically sound datasets that reflect the evolving threat landscape.
+Future research should focus on data augmentation to cover a broader spectrum
+of attack scenarios, improving the robustness of IDS against diverse threats.
+Additionally, fostering open-source datasets and cross-sector collaboration
+is crucial for integrating practical, real-world cybersecurity challenges
+into academic research, thereby democratizing access and promoting innovation
+in IDS development.