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)
Intrusion detection system is still the subject of widespread interest among researchers. Even after years of research, the intrusion detection community still faces a difficult problem. Reducing the number of false positive during the detection process of unknown attack pattern remains an open problem. However, some recent research has shown that there is a potential solution to this problem. Anomaly detection is a key issue in intrusion detection. Disruption of normal operation indicates the presence of attacks, bugs, defects, etc. that may be intentionally or unintentionally induced. This white paper outlines research directions for applying supervised and unsupervised methods to address the problem of anomaly detection. Cited references cover major theoretical issues and guide researchers in interesting research directions
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"ANOMALY DETECTION USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.c345-c351, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304246.pdf
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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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