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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | ENHANCING INTRUSION DETECTION SYSTEM PERFORMANCE LEVERAGING MACHINE LEARNING MODEL AND FEATURE SELECTION |
| Authors Name: | A.Priya , Dr.R.Kannan |
| Download E-Certificate: | Download |
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IJNRD_214775
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| Published Paper Id: | IJNRD2403059 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | ABSTRACT Intrusion detection system (IDS) plays a vital role in mitigating cyber-attacks and exploitations. The internet and communication technology growth have witnessed the attacks and threats are evolving continuously. Intrusion detection systems are designed to capture cyber-attacks efficiently and various types of IDS are studied extensively to capture different forms of exploitation. The performance of any IDS depends on detection and prevention mechanism involved. Though many machine learning techniques are employed, there is still a need to enhance the performance of IDS with respect to rise of newer vulnerabilities in the network. This paper focus on enhancing IDS performance through building machine learning models that leverage feature selection to capture intrusions and anomalies. The proposed model utilizes feature selection to remove redundant and irrelevant features which helps machine learning models to gain better learning. The performance of the proposed model is evaluated on two datasets, namely NSL-KDD and HIKARI-2021 and compared against machine learning models such as LDA, Logistic Regression, NN and SVM. |
| Keywords: | LDA,NSL-KDD,CFSETM,BTRESS,CHISQUARE. |
| Cite Article: | "ENHANCING INTRUSION DETECTION SYSTEM PERFORMANCE LEVERAGING MACHINE LEARNING MODEL AND FEATURE SELECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.a541-a557, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403059.pdf |
| Downloads: | 00053 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2403059 Registration ID: 214775 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: a541-a557 Country: Coimbatore, TamilNadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403059 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403059 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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