Ransomware Detection from Abnormal Behavior Traffic Using The ‎Ensemble Model - Gomathi, S; Anithakumari, K (International Journal of Basic and Applied Sciences, 2025)
Ransomware is a grave online security menace to both personal and business data and information. Computer resource owners can be ‎affected by authentication and privacy breaches, as well as financial damage and reputational damage, in the event of a Ransomware attack. ‎However, the majority of machine learning-based ransomware detector studies are limited by malware obscurity, a lack of a proper analysis ‎ecosystem, incorrect models, and low false-positive rates. To address these issues, this paper proposes Ransomware Detection through ‎Voting and Learning (RDVL) as an ensemble-based approach for ransomware detection. We retrieved the ransomware data from the Kaggle ‎repository in the first place. Then, the collected dataset is normalized with the assistance of L1-Norm Maximization and a Principal ‎Component Analysis (PCA), and the most appropriate ransomware attributes are chosen. Finally, to categorize Ransomware as a subset of ‎the abnormal traffic, RDVL-based ensemble methods are used, including bagging, boosting, and voting. The research aim will be to identify ‎the first instance of a malicious notification of network traffic and its position within a ransomware procedure.‎ It also enables the identification of the activity of Ransomware at an early stage before it is influential. This research uses ransomware ‎abnormal traffic data to complete all the experiments on our proposed framework. As per the outcome of the experiment, the proposed ‎classifier proves to be stronger than other techniques in terms of accuracy scores and sensitivity scores‎.