Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Subject: search.filters.subject.Secure

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Now showing 1 - 4 of 4
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    A Secure and Intelligent Smart Home Controlling System
    (IEEE, 2022-12-26) Hettiarachchi, M. C; Abeysiriwardhana, W. D.C; Aththanayaka, A. M. R. E; Panangala, L. D; Jayawardena, C; Udara, I
    As a result of technological progress, people’s standard of living has improved and is continuing to do so in a variety of ways. Automation is the central idea in contemporary technology, and increasingly, automation technologies are taking the place of human operators. There are currently 4.8 billion people using the internet, or 60% of the global population, and around 87500 people join the internet every single day. The concept of an interconnected network of devices known as the “internet of things” expanded swiftly along with the expansion of the internet user population. The proliferation of IoT-enabled smart home gadgets is largely responsible for this shift. Although technology has advanced, it is still entirely dependent on human input. In addition, the current crop of smart home control systems is narrowly focused, mostly on a handful of security-related tasks such as monitoring, management, and the like. Research into existing solutions revealed that end customers wished for a less complex, more accessible, and cheaper method of controlling their smart homes; hence, we propose a smart home control system that meets these needs while being affordable and user-friendly. This paper presents cutting-edge investigation into the concept of a smart house, which can not only be used to manage appliances but also to open and close gates based on the user’s GPS location and to minimize energy consumption by automatically switching on and off equipment. Furthermore, the produced solution has increased security by implementing a biometric authentication system, a mobile application has been developed on the android platform, and user testing has shown the solution’s significance and validated the proof of concept.
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    IDairy: Intelligence and Secure E-Commerce Platform for Dairy Production and Distribution Using Block Chain and Machine Learning
    (IEEE, 2022-07-18) Liyanage, I; Madhuwantha, N; Perera, M; Ruhunage, S; Mahaadikara, M. D. J. T. H; Rupasinghe, L
    The dairy industry plays an essential role in the Sri Lanka economy. The purpose of this study is to reduce the cost of import dairy products and increase the profit of the dairy industry. IDairy: Intelligence and secure e-commerce platform for dairy production and distribution using blockchain and machine learning has been suggested as a mobile application. As a first step, this research suggested four factors. Develop a business intelligence dashboard using predictive analysis and provide business solutions to dairy companies described the revenue for the coming month using machine learning and the earning data charts for years to come to display in the dashboard. Design IOT device to maintain the temperature of fresh milk cargo while transporting to productions and design smart contract to maintain the optimum temperature for the fresh milk harvest. Develop a system to identify the cows’ diseases using image processing the primary objective was identified cows’ Foot and Mouth diseases and provide notifications to milk farms about existing illnesses. Cows’ disease directly affects dairy productions. Develop a mobile application for farmers to store animal data, do profit calculation, including giving business solutions through the application with location tracking service. With this IDairy application, both farmers and production companies will be able to get an idea about their future profit and will be suggesting the business solutions.
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    Secure Smart Parking Solution Using Image Processing and Machine Learning
    (IEEE, 2022-07-18) Balasuriya, A. I. P; Dilitha, A.G. A. D; Perera, P. A. M. M; Jayaweera, D.K. S; Swarnakantha, N. H. P. R. S; Rajapaksha, U. U. S
    With the IoT connecting the world, finding parking lots is much easier with smart parking solutions. Some of the parking areas in the world use at least some kind of smart parking solution these days. There are existing and broader solutions when it comes to parking a vehicle. So, in this project, we are mainly focusing on developing these existing solutions a step further. I hear we are mainly focusing on a solution that increases the effectiveness of the management processes in the currently existing solutions and developing a relevant and integrated security solution. The project will include a refined prediction system that can predict the outcomes of some of the main processes that are done inside a parking area so that smart parking management and be done much more smoothly and effectively. Prediction systems are designed on the idea of getting possible security issues, vehicle types on peak hours as an outcome. And even in the aspect of security rather than a separate system, there is an integrated one coming with this project that has improved functionality with regards to a parking area. The main possible security threats that happen in a parking area are being identified by us and implemented separate functionalities on detecting them. Furthermore, the project includes a decision-making system that can deduce and take the decision on slot management in a parking area so it can work effectively and with minimum human interaction.
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    Secure Web Navigation with Intrusion Detection And Quota Management for SOHO and Small Scale Businesses
    (IEEE, 2019-12-05) Perera, M. A. D. S. R; Hemapala, C; Udugahapattuwa, M; Senarathne, A. N
    It's a modern day necessity and a trend to offer free and open web access to their customers and employees in small scale and Small Office Home Office (SOHO) business culture (restaurants, malls, coffee shops). Unfortunately, internet security and quota management are mostly overlooked which makes it an intruders' paradise. The existing solutions that incorporate machine learning based dynamic aspects, cannot be afforded by our target audience nor do they possess the extensive IT knowledge to configure and maintain them. To cater to this gap, this research proposes the network management device `Dynamic Defender', targeted for small scale and SOHO type businesses which focuses on affordability and user-friendliness as key factors while incorporating cutting edge machine learning technologies. The Dynamic Defender's Intrusion Detection Engine is comprised of 99.13% accuracy with its base run on Artificial Neural Networks. URL Classification Engine produced high accuracy on all 3 machine learning algorithms which were used. Specifically, Random Forest with 92.94 % precision, Artificial Neural Networks with 90.33% precision and Logistic Regression with 91.41% precision. The Dynamic Bandwidth Management System consisted of an 89% accuracy level on the hybrid module of Linear Regression and Decision Trees while the Quota Management System (QMS) provided an accuracy level of 82% in K-Nearest Neighbors and 89% on Decision Tree algorithm.