Study of data collection, intrusion and detection using fuzzy logic

Nabonarayan Jha , Jaynarayan Jha , K. B. Singh

NIDS, IDSS, AIIDS, HTML, ASP, PHP, JVM, OS, API, ABIDS, HIDS

The design of a Network Intrusion Detection System (NIDS) is a delicate process which requires the successful completion of numerous design stages. The feature selection stage is one of the first steps that needs to be addressed, and can be considered among the top most important ones. If this step is not carefully considered the overall performance of the NIDS will greatly suffer, regardless of the detection technique, or any other algorithms that the NIDS is using. The most common approach for selecting the network features is to use expert knowledge to reason about the selection process. However, this approach is not deterministic, thus, in most cases researchers end-up with completely different sets of important features for the detection process. Furthermore, the lack of a generally accepted feature classification schema forces different researchers to use different names for the same (subsets of) features, or the same name for completely different ones. In this paper we present about the data collection, intrusion and detection using fuzzy system network.

Published Paper   E-Certificate

"Study of data collection, intrusion and detection using fuzzy logic", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 9, page no.11 - 16, September-2020, Available :https://ijsdr.org/papers/IJSDR2009002.pdf

Volume 5 Issue 9, September-2020

Pages : 11 - 16

Paper Reg. ID: IJSDR_192397

Published Paper Id: IJSDR2009002

Downloads: 000347193

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2009002

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2009002

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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