MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02833nam a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111037.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250502t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010022 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .N33 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Muhammad Nadzmi Bin Md Azam, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Reported malicious codes incident within malaysia’s landscape: time series modelling and a timeline analysis / |
| Statement of responsibility, etc. |
Muhammad Nadzmi Bin Md Azam |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 49 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
The advancement of technology is such a marvel in these modern days. As countries embrace the vast progress of cyber-technology, the risk of cyber threats increases. Malicious codes have been one of the most menacing threats in the cyberspace, where they can damage and cost hefty damage to individuals, organisations, and governments Hence, Majlis Keselamatan Negara has developed a strategy which is known as Malaysia Cyber Security Strategy (MCSS). This strategy has four pillars to protect and sustain the perimeter of cybersecurity in Malaysia and one of the pillars is building awareness and education about cybersecurity. As an effort to align with the pillar, research involving reported malicious codes in Malaysia’s monthly data is conducted. The data will be analysed to see the outliers and recognise what the crucial factor of the outliers in the data is. Then, the outliers will be investigated, and the findings will be constructed chronologically for the timeline analysis. The data also will be forecasted to predict the trend from May 2022 until December 2024. The predictive algorithms proposed for this research are Autoregressive integrated moving average (ARIMA), Long Short Term Memory (LSTM), and NeuralProphet. The best model is chosen by the least values of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The outcome of this research is presented in an interactive dashboard as a deployment of this project. The dashboard is the effort to provide information on malicious codes incidents in Malaysia |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Centre for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Project |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |