Intrusion detection framework using fuzzy clustering and fuzzy neural network
| dc.contributor.guide | Balasubramanie P | |
| dc.coverage.spatial | fuzzy clustering and fuzzy neural network | |
| dc.creator.researcher | Ananthi P | |
| dc.date.accessioned | 2016-11-03T08:18:06Z | |
| dc.date.available | 2016-11-03T08:18:06Z | |
| dc.date.awarded | 31/10/2015 | |
| dc.date.completed | 01/10/2015 | |
| dc.date.registered | n.d. | |
| dc.description.abstract | Now a days there is a tremendous growth of computer networks that have become ultimate devices for commercial social and military sectors The vast usage of networks and high accessibility of Internet eventually raise security breaches In a networked environment fraudulent and mischievous individuals endeavour to compromise the integrity confidentiality and availability of resources So network security is a major concern of every organization in order to protect the systems from unanticipated abuses/attacks Therefore it is imperative to detect and prevent malicious activity with the deployment of various security techniques Several conventional protection approaches similar to user authentication information encryption access control and firewalls are used as modes of preliminary protection for networks Intrusion is a deliberate attempt to exploit vulnerability of system resources abuse privileges and gain illegitimate access to network resources Although wide range of security expertise guard network based systems still a lot of undetected intrusions continue to materialize Hence effective intrusion detection mechanism is essential to prevent malicious events newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | None | |
| dc.format.dimensions | 21cm. | |
| dc.format.extent | p.210-221 | |
| dc.identifier.uri | http://hdl.handle.net/10603/119991 | |
| dc.language | English | |
| dc.publisher.institution | Faculty of Science and Humanities | |
| dc.publisher.place | Chennai | |
| dc.publisher.university | Anna University | |
| dc.relation | p.210-221 | |
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Fuzzy Clustering | |
| dc.subject.keyword | Fuzzy Neural Network | |
| dc.subject.keyword | Intrusion Detection | |
| dc.subject.keyword | Neural Network | |
| dc.subject.keyword | Science and Humanities | |
| dc.title | Intrusion detection framework using fuzzy clustering and fuzzy neural network | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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