000			a
999			_c10101 _d10101
003			OSt
005			20191116100138.0
008			191116b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_910506 _aBrahma, Anitarani
245			_aDatabase Intrusion Detection Using Genetic Support Vector Fuzzy Clustering Learning Model
250			_aVol.6(2), May-Aug
260			_aNew Delhi _bSTM Journals _c2019
300			_a32-40p.
520			_aThe rapid development of computer networks and increasing dependency of almost all companies and government agencies on Internet and cloud computing lead to the problem of stability and security like intrusions in several forms which can cause huge loss to these organizations. During recent years, disaster in data due to intrusions has dramatically increased. The hindrance of such intrusions is entirely dependent on their detection part which can be possible through a high-performance based intrusion detection system in database which has higher accuracy rate and negligible false positive rate. As part of funded effort in database security, soft computing proven to be capable of creating a system capable of detecting and characterizing anomalous behaviour which is composed of evolutionary computing tools with artificial neural networks and/or fuzzy logic. In this progression, here we present a Database Intrusion Detection System, by applying Genetic Algorithm for feature extraction and Fuzzy clustering and Support Vector Machines are used for detection purpose to efficiently detect insider threat with a reasonable false positive rate.
650		0	_94622 _aComputer Engineering
700			_910507 _aPanigrahi, Suvasini
773	0		_dNoida STM Journals _tJournal of artificial intelligence research and advances (JoAIRA)
856			_uhttp://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=2087 _yClick here
942			_2ddc _cAR