Machine Learning Based Malware Trend Analysis using Cyber Threat Intelligence Data

The number of cyberattacks and malware-related risks on a variety of online platforms has dramatically expanded due to the quick development of digital technology. Because cyber threats are always changing, traditional cybersecurity techniques frequently fail to identify new virus patterns. Threat detection systems can be improved by using Cyber Threat Intelligence (CTI), which offers useful information on malicious activity, attack behaviours, and threat actors. This study offers a machine learning-based method for utilising cyber threat intelligence data to analyse malware trends. The suggested approach makes use of a dataset that includes information about threats and uses data pretreatment methods to get the data ready for model training. To find harmful trends in the dataset, three machine learning algorithms—Random Forest, Support Vector Machine, and Decision Tree—are put into practice and assessed. Evaluation criteria like precision, recall, F1-score, and accuracy are used to gauge these models' performance. According to experimental results, the Random Forest model outperforms all other examined algorithms in terms of accuracy, indicating its efficacy in identifying dangers related to malware. Users can effectively carry out training and prediction tasks thanks to the system's deeper integration into a web-based interface. The suggested method helps security experts recognise new malware trends moresuccessfu lly and advances cyber threat analysis. Keywords:Cyber Threat Intelligence, Malware Detection, Machine Learning, Random Forest, Support Vector Machine, Decision Tree, Cybersecurity Analytics, Threat Pattern Analysis, Malware Trend Detection, Data-Driven Security Analysis.