Faculty of Computing

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Author: search.filters.author.Liyanapathirana, CSubject: search.filters.subject.Machine Learning

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    PublicationOpen Access
    Development of Cyber Threat Intelligence System in a SOC Environment for Real Time Environment
    (Department of Computing and Information Systems, Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka, 2021-02-24) Varatharaj, A; Rupasinghe, P. L; Liyanapathirana, C
    Now a days, Information Communication Technology (ICT) plays an important role in the world. In IT, Cyber Security holds a vast place. Cyber Threat Intelligence (CTI) leads the significant place within Cyber Security, as many Cyber Threats need to be faced every day by a particular organization. Security Operation Center (SOC) helps to monitor and analyze an organization’s security position in Real Time. This paper proposes about the Cyber Threat Intelligence framework in a SOC Environment in Real Time. The proposed framework contains of three layers, which are built above Security Onion. The Layer 1 comprises of input data from online and offline sources. In Layer 2, implemented two components namely Filter data and Cut down data, which receive the data from Layer 1. Finally, in Layer 3 delivers a detailed report. As the input for the Layer 1, Financial Datasets is used. These Financial Datasets, which helps in order to detect the Financial Frauds. Machine Learning is used to train the model. By implementing CTI System in an organization, it helps to gain predictive output regarding the upcoming threats. Also, it helps to ensure the reputation of an organization by establishing trust between the users. Helps to increase the number of customers to an organization. The above are the advantages gained by a particular organization by having a CTI System.
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    PublicationEmbargo
    An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning
    (IEEE, 2020-12-10) Jayaweera, K. N; Kallora, K. M. C; Subasinghe, N. A. C. K; Rupasinghe, L; Liyanapathirana, C
    According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.