| 000 -LEADER |
|---|
| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20191116100138.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
191116b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
10506 |
| Author |
Brahma, Anitarani |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Database Intrusion Detection Using Genetic Support Vector Fuzzy Clustering Learning Model |
| 250 ## - EDITION STATEMENT |
|---|
| Volume, Issue number |
Vol.6(2), May-Aug |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New Delhi |
| Name of publisher, distributor, etc. |
STM Journals |
| Year |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Pagination |
32-40p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
4622 |
| Topical term or geographic name entry element |
Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
10507 |
| Co-Author |
Panigrahi, Suvasini |
| 773 0# - HOST ITEM ENTRY |
|---|
| Place, publisher, and date of publication |
Noida STM Journals |
| Title |
Journal of artificial intelligence research and advances (JoAIRA) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
http://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=2087 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
