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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Enhancing the Efficacy of Intrusion Detection Systems through Utilization of Machine Learning Classifiers |
| Authors Name: | S.P.SENTHILKUMAR , Dr.Aranga.Arivarasan |
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IJRTI_188641
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| Published Paper Id: | IJRTI2312021 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | In the realm of computer network security, the identification and mitigation of security threats are of utmost importance. In network security, IDS are vital for detecting and preventing unauthorized access or malicious activities in a network. However, with the persistent and ever-expanding complexity and diversity exhibited by modern cyber-attacks, there is a pressing need to adopt increasingly sophisticated approaches. This research paper undertakes an in-depth investigation into the utilization of machine learning classifiers to augment the efficacy of IDS, specifically focusing on harnessing the potential of the CIC IDS 2017 dataset. This study aims to extensively evaluate two widely recognized machine learning classifiers, namely K-Nearest Neighbors (KNN) and Decision Trees. These classifiers are assessed based on their inherent capability to successfully detect intrusions within the extensive expanse of the CIC IDS 2017 dataset. This voluminous dataset encompasses a broad spectrum of meticulously collected network traffic data, encompassing both regular and malicious activities, thereby reflecting the diverse landscape of real-world cyber threats. The Decision Tree classifier emerges as the leader with an impressive accuracy rate of 96.72%, followed closely by the KNN classifier at 95.94%. These findings underscore the immense potential of machine learning algorithms in discerning network intrusions with exceptional precision. These outcomes significantly contribute to advancing intrusion detection systems, paving the way for intelligent solutions that proactively counter evolving cyber threats. |
| Keywords: | Keywords: Intrusion Detection Systems (IDS), CIC IDS 2017 dataset, network traffic data, Security threats, K-Nearest Neighbors (KNN), Decision Trees, malicious activities. |
| Cite Article: | "Enhancing the Efficacy of Intrusion Detection Systems through Utilization of Machine Learning Classifiers", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 12, page no.146 - 158, December-2023, Available :http://www.ijrti.org/papers/IJRTI2312021.pdf |
| Downloads: | 000205132 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2312021
Registration ID:188641
Published In: Volume 8 Issue 12, December-2023
DOI (Digital Object Identifier):
Page No: 146 - 158 Country: CHIDAMBARAM, TAMILNADU, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2312021 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2312021 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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