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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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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Volume 10 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 108
Article Submitted : 15075
Article Published : 6744
Total Authors : 18064
Total Reviewer : 658
Total Countries : 121
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| Paper Title: | Anomaly Based Malware Detection Using Machine Learning |
| Authors Name: | P.Muthu Pandiyan , S.Raja , L.Kumaresan , B.Gokul , R.Loganathan |
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IJRTI_190284
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| Published Paper Id: | IJRTI2409003 |
| Published In: | Volume 9 Issue 9, September-2024 |
| DOI: | |
| Abstract: | Malware detection is a critical aspect of cybersecurity, with traditional signature-based methods proving inadequate against evolving threats. This journal explores anomaly-base detection using machine learning to identify malicious activities by recognizing deviations from normal behavior. The proposed system leverages machine learning algorithms to detect unknown and zero-day malware, enhancing cybersecurity by adapting to new threat patterns. The study examines the operational, economic, and technical feasibility of implementing this system in real-world environments.By integrating advanced machine learning techniques with anomaly-based detection, the proposed system represents a significant advancement in the field of cybersecurity. It aims to provide a more robust defense mechanism against emerging threats, offering enhanced protection against both known and unknown malware. This Journal not only contributes to the development of cutting-edge security technologies but also provides valuable insights into the practical considerations of implementing these systems in dynamic and complex real-world environments. |
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| Cite Article: | "Anomaly Based Malware Detection Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 9, page no.26 - 31, September-2024, Available :http://www.ijrti.org/papers/IJRTI2409003.pdf |
| Downloads: | 000204843 |
| 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: IJRTI2409003
Registration ID:190284
Published In: Volume 9 Issue 9, September-2024
DOI (Digital Object Identifier):
Page No: 26 - 31 Country: Namakkal, Tamil Nadu, India Research Area: Other Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2409003 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2409003 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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