Browsing by Author "Peiris, A"

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A Sysmon Event-based Machine Learning Approach to Ransomware Detection
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Peiris, A; Kumara, S; Peiris, T; Ramanayake, H; Abeywardena, K,Y; Wijesooriya, A
The rapidly increasing cyber threat landscape has caused a huge impact on a large number of sectors worldwide, and among them ransomware is one of the most critical threats that should be controlled effectively. This paper presents a solution for detecting ransomware by leveraging features extracted from Windows Sysmon event logs, combined with machine learning based classification. A minimal set of key Sysmon events are collected and a comprehensive set of features are generated from it by a lightweight endpoint agent. These feature vectors are then transmitted to a cloud-based inference model for real time analysis, which ensures low overhead on the endpoints while allowing for a high scalability. Four machine learning models were evaluated on a publicly available dataset and XGBoost was selected as the suitable model for this solution due to its high accuracy while maintaining the fastest inference speed. Overall, this solution is capable of detecting ransomware with high precision and a low false negative rate, while maintaining fast inference times and a minimal processing overhead on the endpoints. The architecture enables scalable, real-time ransomware detection suitable for both enterprise and individual environments.
Embargo
Optimum Music: Gesture Controlled, Personalized Music Recommendation System
(IEEE, 2021-12-09) Wijekoon, R; Ekanayaka, D; Wijekoon, M; Perera, D; Samarasinghe, P; Seneweera, O; Peiris, A
Music plays an important role in everyone’s life since it helps to relax the mind when appropriate music is played. This paper presents a music recommendation system based on the user’s current emotions, activities as well as demographic information such as age, gender, and ethnicity. In addition, the system can be controlled by hand gestures and vocal commands. Unsupervised learning methods in were used to recommend music according to the demographic data and emotions of the user. Finally, the important idea is to recommend music based on all of the user’s data, such as demographics, emotions, and activities. The overall system performance was manually tested and evaluated with a group of individuals, yielding a 70% satisfaction rate for the recommendation; additionally, supporting models such as demographic identification, emotion identification, and hand gesture identification have received a higher proportion of accuracies, contributing to the research’s success. Unlike other systems, ours utilizes all of the user’s information while making music recommendations.