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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: | Network Intrusion Detection Using Supervised Machine Learning |
| Authors Name: | Marelly shivaram , k.sravanthi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_209853
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| Published Paper Id: | IJNRD2312005 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | Contrasted with the past, improvements in PC and correspondence innovations have given broad and propelled changes. The use of new innovations give incredible advantages to people, organizations, and governments, be that as it may, messes some up against them. For instance, the protection of significant data, security of put away information stages, accessibility of information and so forth. Contingent upon these issues, digital fear based oppression is one of the most significant issues in this day and age. Digital fear, which made a great deal of issues people and establishments, has arrived at a level that could undermine open and nation security by different gatherings, for example, criminal association, proficient people and digital activists. Along these lines, Intrusion Detection Systems (IDS) has been created to maintain a strategic distance from digital assaults. Right now, learning the bolster support vector machine (SVM) calculations were utilized to recognize port sweep endeavors dependent on the new CICIDS2017 dataset with 97.80%, 69.79% precision rates were accomplished individually. Rather than SVM we can introduce some other algorithms like random forest, Decision Trees, Logistic Regression where these algorithms can acquire accuracies more accuracy. |
| Keywords: | Intrusion detection |
| Cite Article: | "Network Intrusion Detection Using Supervised Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.a24-a33, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312005.pdf |
| Downloads: | 000118754 |
| 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:IJNRD2312005 Registration ID: 209853 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: a24-a33 Country: Warangal, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312005 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312005 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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