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Author: search.filters.author.Senarathne, A. N.Subject: search.filters.subject.Machine Learning

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    Smart Office Automation System for Covid Prevention
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rajapaksha, R.A.D.S.; Costa, L.S.; Prasanna, P.L.U.S.C.; Disanayaka, A.P.D.; Senarathne, A. N.; Wijekoon, Janaka L.
    Today, this coronavirus is spread all around the world. Most organizations and businesses start to think about how to continue their business in a situation like COVID-19 and their employees’ health and business security. To avoid and be safe from this type of disease, there are some common rules to follow. Keeping a distance, wearing a mask, cleaning our hands, are some health guidelines from them. According to the current situation, many inventors are trying and have already given some solutions to avoid these kinds of situations aligning with health guidance’ provided by WHO. With the advantage of advanced modern-day technologies and ideas, researchers started to think about how to face situations like these with the new technologies and found that many users are highly interested and motivated with automated systems. Thus, from this study, we aim to provide a fully automated office management system to prevent corona with advanced technology in combination with IoT technologies, Machine learning, Cloud technologies, and sensor technologies. Considering the security aspect, Controlling the main entrance, identifying, ensuring user’s authentication before entering the building, and monitoring employee activities are very significant aspects of the study. As the result of the study, the combination of IoT technologies and Machine Learning with deep learning mechanisms have guaranteed organizational business continuity, employees' health, and security.
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    Event-Driven Malicious URL Extractor
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jonathan, S.W.S.; Arunaasalam, R.H.; Senarathne, A. N.; Wishvajith, V.; Ramanayaka, A.M.; Yapa, K.
    Cyber-attacks are attacks that are commonly carried out in order to obtain sensitive information or disrupt internet-based services. Recent occurrences, both internationally and locally, have shown an influx of these attacks expanding rapidly through the use of malicious URLs (Uniform Resource Locators). Traditional measures, including such blacklisting malicious URLs, make it extremely difficult to respond to such attacks in a timely and efficient manner. Most existing solutions remain restricted in terms of scalability and proactive user safeguarding in situations when freshly formed URLs are correlated with a recent event, such as Covid-19 related frauds. The proposed solution is presented with the primary aim of addressing traditional system limitations and offering an interface for users to protect themselves by detecting phishing/malicious URLs in real time. In this research, we will examine extracting user-input eventrelated keywords and leveraging NLP (Natural Language Processing) algorithms to match them with the accompanying URL (Uniform Resource Locator) token data to determine whether the URLs are malicious or benign.