02833nam a2200337 i 4500 0 0 lcc 0 6 10000 10000 2025-05-02 0 CD13575 T000003175 2025-05-02 00:00:00 2025-05-02 PSM 102487 102493 MY-KuUP 20251125111037.0 t||||fs|||| 000 0 ta 250502t20232023my a|||fs|||| 000 0 eng d THE0010022 (Local) Hardback UMPSA eng UMPSA rda PSM .N33 2023 r Bc. Muhammad Nadzmi Bin Md Azam, author. Reported malicious codes incident within malaysia’s landscape: time series modelling and a timeline analysis / Muhammad Nadzmi Bin Md Azam Kuantan, Pahang : UMPSA, 2023 © 2023 xi, 49 pages : illustrations ; 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 Includes bibliographical references 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 Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project Dissertations lcc PSM