Database Intrusion Detection Using Genetic Support Vector Fuzzy Clustering Learning Model
By: Brahma, Anitarani
.
Contributor(s): Panigrahi, Suvasini.
Publisher: New Delhi STM Journals 2019Edition: Vol.6(2), May-Aug.Description: 32-40p.Subject(s): Computer EngineeringOnline resources: Click here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: The 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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2020169 |
The 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.
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