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Imbalanced Intrusion Detection System Datasets</title> <meta name="description" content="A Survey in Techniques for Imbalanced Intrusion Detection System Datasets"> <meta name="keywords" content="IDS, intrusion detection system, imbalanced datasets, sampling algorithms, big data."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Survey in Techniques for Imbalanced Intrusion Detection System Datasets"> <meta name="citation_author" content="Najmeh Abedzadeh"> <meta name="citation_author" content="Matthew Jacobs"> <meta name="citation_publication_date" content="2023/01/04"> <meta name="citation_journal_title" content="International Journal of Computer and Systems Engineering"> <meta name="citation_volume" content="17"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="9"> <meta name="citation_lastpage" content="18"> <meta 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href="https://publications.waset.org/search?q=Matthew%20Jacobs"> Matthew Jacobs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.</p> <iframe src="https://publications.waset.org/10012884.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=IDS" title="IDS">IDS</a>, <a href="https://publications.waset.org/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/search?q=imbalanced%20datasets" title=" imbalanced datasets"> imbalanced datasets</a>, <a href="https://publications.waset.org/search?q=sampling%20algorithms" title=" sampling algorithms"> sampling algorithms</a>, <a href="https://publications.waset.org/search?q=big%20data." title=" big data."> big data.</a> </p> <a href="https://publications.waset.org/10012884/a-survey-in-techniques-for-imbalanced-intrusion-detection-system-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012884/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012884/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012884/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012884/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012884/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012884/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012884/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012884/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012884/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012884/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1125</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Vaibhav Jayaswal, "Dealing with Imbalanced dataset”, https://towardsdatascience.com/dealing-with-imbalanced-dataset-642a5f6ee297, Oct 18, 202 <br>[2] K. 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