Browsing by Author "Ranasinghe, A"

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Enhanced Secure Solution for PoS Architecture
(IEEE, 2019-12-05) Samaranayake, C; Kuruppu Achchige, R. P; Shanaz, T; Ranasinghe, A; Senarathne, A. N
Today retail businesses expect to bring the utmost in sales and payment transactions by adapting new technologies. Therefore, Advanced Point of Sales (PoS) Systems are widely used in the industry. Regardless of how efficient and secure these systems or applications work, unexpected information security risks can arise. Such risks could be a threat to their business and organization. It is important to ensure that critical information such as payment card information, handled in PoS systems is kept secure from attacks that could bring financial loss. This research provides a solution by studying the overall infrastructure of a PoS System and identifies the key events that such data would be at risk. The major concern of it was to enhance the existing security features of the system to avoid any type of malicious activity. This research consists of four main sections under security related to PoS Systems that would address the risk; Studying of malware and classifying them, detecting possible attacks and means of preventing it, a robot (BOT) to predict and generate the system status with a Data Leakage Prevention(DLP) solution for all the events occurring at a PoS. The key objective of implementing this solution was to protect the confidential data that is being used in the PoS System and to avoid threats that lead to the unavailability of the system. The implemented security features using machine learning and Deep Learning methods to the existing PoS functions produced a 99.3% of accuracy in Malware Detection and 95% of accuracy in its Classification process while the DLP Solution was able to obtain an accuracy of 84.6%. The above results retrieved fulfilled the research objectives and aided to integrate an enhanced security solution for a PoS system.
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PROBEXPERT: An Enhanced Q&A Platform for Reducing Time Spent on Learning and Finding Answers
(IEEE, 2022-07-18) Thennakoon, K; Ekanayake, D; Marapana, T; Ranasinghe, A; Wijendra, D. R; Gamage, A
The World Wide Web contains a wide range of material from a variety of fields. However, when concerns towards the computer science domain, information users find on the internet may not be up-to-date due to the rapid pace of change and having to spend less time on the internet for researching and debugging tasks is an added luxury. Having an expertise level while providing answers through a platform is convenient for users, yet when a user signs into a platform, the user must start from the beginning, regardless of the level of competence in the field. Moreover, not having a proper way to evaluate the existing programming knowledge is another obstacle. To address mentioned complications, researchers of this paper have introduced a new e-learning platform- ‘ProbExpert’. The platform has been constructed with machine learning and deep learning approaches such as NLP, keyword extraction, semantic information analysis, cosine similarity, and information summarization. With aforesaid technologies, ProbExpert provides systems in automated answering, optimized answer generation, structured question-based quiz evaluation together with a fully automated portfolio generation with a novel user ranking algorithm based on the bell curve.