Browsing by Author "Senarathne, A. N."

Now showing 1 - 4 of 4

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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.
Embargo
Secure Messaging Platform Based on Blockchain
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ellewala, U. P.; Amarasena, W.D.H.U; Lakmali, H.V.S.; Senanayaka, L.M.K.; Senarathne, A. N.
The boundaries between personal and business communications is a key issue faced by most organizations. Use of unsecured and unsafe applications in workplaces pose enormous security risks. Companies are not adequately aware about the applications that are being used in their employees' devices. When it comes to critical business communication involving exchanging trade secrets, making business referrals and strategic business decisions, protecting of messages and shared files becomes a challenge. Most publicly available communication platforms do not empower organizations to regulate, track and scale their communication and does not provide compliance with data protection frameworks, which can result in cross industry system risks. As a result, both individuals and organizations express deep concern about data security and protection of privacy when using Instant Messaging applications. Non-repudiation in communications not only conveys to the user, recognition of the communication process, but it is also a crucial way to establish a relationship of trust and to overcome trust disputes. Our primary objective, through this research, is to develop a chat application with more secure channels of enterprise level communication. Using new technologies such as blockchain, which operate on a decentralized model, we can surmount the drawbacks of traditional messaging applications, thereby ensuring confidentiality, integrity and availability of official data, along with advanced auditing features.
Embargo
A Sensitive Data Leakage Detection and Privacy Policy Analyzing Application for Android Systems (PriVot)
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Atapattu, H.N.; Fernando, W.S.N.; Somasiri, J.P.A.K.; Lokuge, P.M.K.; Senarathne, A. N.; Tissera, M.
Mobile applications can have access to various sensitive information to accomplish the business requirements as well as user requirements. Due to the sensitivity of this information, app developers are bound by the regulations to provide a privacy policy that describes their data collection practices. However, there were many incidents where the privacy policies were inconsistent with the actual data practices. Additionally, the privacy policies are often too long and difficult to grasp just by reading them due to their complex language. To address this hurdle, we propose a mobile application “PriVot”. PriVot has a privacy policy analyzer built with a hierarchical classifier using convolutional neural networks to provide a detailed and unambiguous summary indicating the data that is being collected by each app and their purpose for being collected Furthermore, it monitors the network traffic of the device with the aid of a Transport Layer Security(TLS) proxy, a Forwarder, and a Traffic Analyzer that operates on-device without requiring root privileges to identify potential data leakages and privacy policy violations. We present "PriVot" which achieved a 67.4% accuracy on privacy policy analysis and a 72.5% throughput at a low latency overhead with the network traffic monitoring.
Embargo
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.