Reported malicious codes incident within Malaysia’s landscape: Time series modelling and a timeline analysis - Md Azam, Muhammad Nadzmi; Ramli, Nor Azuana (AMCS Research Centre, 2022)
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. This research aims to investigate the outliers in the dataset timeline analysis. The data will be analysed to see the outliers and recognize 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 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 results from the analysis showed that the best forecasting model is LSTM and from the forecasted data using this model, it can be seen the trend of incident increases until 2023, then decreases to 2024.