MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03659ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125105435.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
191128t20192019my a|||frm||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0008333(Local) |
| Qualifying information |
hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FSKKP .H37 2019 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ali, Mohammed Hasan, |
| Relator term |
author. |
| 245 20 - TITLE STATEMENT |
|---|
| Title |
A fast learning network with improved particle swarm optimization for intrusion detection system/ |
| Statement of responsibility, etc. |
Mohammed Hasan Ali |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2019 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 131 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computer Systems and Software Engineering |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2019 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
In current days the intrusion detection systems (IDS) have several shortcomings such as high rates of false positive alerts, low detection rates of rare but dangerous attacks, and the need for a constant human intervention and tuning. Daily, there are reports of incidents such as major ex-filtration of data for the purposes of stealing identities, credit card numbers, and intellectual properties, as well as to take control of network resources. Machine learning approaches have been widely used to increase the effectiveness of intrusion detection platforms. While some machine learning techniques are effective at detecting certain types of attacks, there are no known methods that can be applied universally and achieve consistent results for multiple attack types. This situation makes the detection of cyber-based attacks on computer networks a relevant and challenging area of research. The Fast Learning Network (FLN) is one of the new machine learning algorithms that are easy to implement, computationally efficient, and with excellent learning performance characteristics. However, the internal power parameters (weight and basis) of FLN are initialized at random, causing the algorithm to be unstable. In this work, a new cooperative multi-swarm scheme called multi-swarmoptimization (MRPSO) which is inspired by the human social behavior was proposed for the interaction of several PSO groups while searching for the best parameters values of PSO. The focus of this research is on the development of a model that can optimize the initial parameters of FLN based on MRPSO to obtain an optimal set of initial parameters for FLN, thus, creating an optimal FLN classifier named as MRPSO-FLN which can improve the efficacy of network intrusion on data sets that contain instances of multiple classes of attacks. These methods were tested on NSL-KDD intrusiondetection datasets and the results indicate that the proposed approaches used in the system performed well in large dataset processing. In these experiments, it was demonstrated that the FLN optimization method achieved 0.9964 which is a higher accuracy than most of the existing paradigms for classifying network intrusion detection data. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computer Systems and Software Engineering |
| General subdivision |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |