Research Publications
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Publication Embargo An Analysis on Different Distance Measures in KNN with PCA for Android Malware Detection(IEEE, 2022-11-30) Dissanayake, S; Gunathunga, S; Jayanetti, D; Perera, K; Liyanapathirana, C; Rupasinghe, LAs Majority of the market is presently occupied by Android consumers, Android operating system is a prominent target for intruders. This research shows a dynamic Android malware detection approach that classifies dangerous and trustworthy applications using system call monitoring. While the applications were in the execution phase, dynamic system call analysis was conducted on legitimate and malicious applications. Majority of relevant machine learning-based studies on detecting android malware frequently employ baseline classifier settings and concentrate on selecting either the best attributes or classifier. This study examines the performance of K Nearest Neighbor (KNN), factoring its many hyper-parameters with a focus on various distance metrics and this paper shows performance of KNN before and after performing Principal Component Analysis (PCA). The findings demonstrate that the classification performance may be significantly improved by using the adequate distance metric. KNN algorithm shows decent accuracy and improvement of efficiency such as decreasing the training time After PCA.Publication Embargo Anomaly Detection in Microservice Systems Using Autoencoders(IEEE, 2022-12-09) de Silva, M; Daniel, S; Kumarapeli, M; Mahadura, S; Rupasinghe, L; Liyanapathirana, CThe adaptation of microservice architecture has increased massively during the last few years with the emergence of the cloud. Containers have become a common choice for microservices architecture instead of VMs (Virtual Machines) due to their portability and optimized resource usage characteristics. Along with the containers, container-orchestration platforms are also becoming an integral part of microservice-based systems, considering the flexibility and scalability offered by the container-orchestration media. With the virtualized implementation and the dynamic attribute of modern microservice architecture, it has been a cumbersome task to implement a proper observability mechanism to detect abnormal behaviour using conventional monitoring tools, which are most suitable for static infrastructures. We present a system that will collect required data with the understanding of the dynamic attribute of the system and identify anomalies with efficient data analysis methods.Publication Embargo Smart Advertising Based on Customer Preferences and Manage the Supermarket(IEEE, 2022-12-09) Wickramasinghe, A.Y.S. W; Eishan Dinuka, W.H.A.; Weerasinghe, W.S. H; Karunaratne, K.P. G; Liyanapathirana, C; Rupasinghe, LAs a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users’ feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users’ preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly.Publication Embargo A Drone-Based Approach for Deforestation Monitoring(IEEE, 2022-12-29) Nuwantha, M. B; Jayalath, C. N; Rathnayaka, M.P; Fernando, D. C; Rupasinghe, L; Chethana, MMost importantly the forests play a major role in providing worldwide oxygen and other essentials necessity. Monitoring the forest cover from above the forest canopy level can be easily done by retrieving images from the space satellites. Yet, it’s a great challenge to identify the deforestation as they are more complex. To overcome the complexity, the need of taking images from a considerable height is important. To do this part this research shows that unmanned ariel vehicles as knows as drones can do it conveniently and assist the process accurately. Monitoring the forest cover using drones is accurate but its challenging to break the barriers such as discovering objects and filtrate them to parts to process the correct data to arbitration as output. In this research project planned to design the image processing mechanism to success those mention obstacles to give successful output. To contribute the development of this research project in here using more effective approaches mostly using drones and automated software solution with getting help of less manpower on it. Utilization of the monitoring process is more effective with the real time image processing from the drone footages taken from the targeted site with the help of the software. The research is expecting the final output should be much as effective. Finally, this research project is scoping to track deforestation and we evaluated current literature on drone environmental applications, including forest monitoring, and drew on our own practical experience flying tiny drones to map and monitor tropical forests. Also, this project believes that the use of small drones can assist tropical communities in better managing and conserving the forests, while also benefiting partner organizations, governments, and forest data end-users, particularly those involved in forestry, biodiversity conservation, and climate change.Publication Embargo A Notion of Real-Time Anomaly Detection for IoT Devices Based on Hardware-Level Performance(Institute of Electrical and Electronics Engineers, 2022-11-03) Umagiliya, T; Senarathne, A; Rupasinghe, LInternet of Things (IoT) is becoming a considerable topic due to its benefits in the modern world. IoT devices carry out simple routine duties, but they can be valuable. IoT devices or a group of devices are connected to the internet, anomaly detection is essential, considering securing the IoT devices within the isolated environments. The most known and typical attacking modes for IoT devices are denial-of-service (DoS) and password brute-force attacks. The most dangerous attack is the Zero-day attack. The best mechanism for finding those issues as a solution is the concept of anomaly detection. Considering IoT device hardware-level anomaly detection mechanism uses the heat and the power consumption for detections. The results of those concepts can be misleading due to environmental situations. Here, it discusses the distinct approach to merely overcoming those problems using CPU and RAM utilization and driving the solution efficiently and effectively up to 99.9%.Publication Embargo Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach(IEEE, 2022-10-11) Panagoda, D; Malinda, C; Wijetunga, C; Rupasinghe, L; Bandara, B; Liyanapathirana, CThis research takes us forward with the concepts of Federated Learning and SDN to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital ICEs, the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.Publication Embargo 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, LThe 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.Publication Embargo Multilingual Conversational AI incorporated with Visual Questions Answering and Intelligent Disease Prediction for Healthcare Industry(IEEE, 2022-07-18) Sasmitha, N. U. A.; Wathasha, H. K. G. V.; Guruge, P. P. L.; Silva, W. J. T.; Rupasinghe, L; Gunarathne, G. W. D. A.Artificial intelligence (AI) is becoming more active than ever in everyday life and steadily being incorporated to healthcare. AI, with its seemingly limitless power, affirms a promising future to a revolutionized healthcare system. This paper is proposing a conversational AI solution in two different languages, English and Sinhala, to predict diseases through a conversation, a visual question answering solution to generate answers are based on a given question and a medical image and a disease forecasting module. A robust, accurate prediction is a rather difficult task given the availability of data and absence of preprocessed, clean data. With the aid of outlier rejection, data imputation, vectorization, feature selection and data standardization, the proposed framework gets the advantage of latest machine learning advancements such as AI using DIET classifier and NLU pipelines, for the conversational disease diagnosis which uses support vector machine (SVM) achieved an accuracy of 0.93. Moreover, the visual questions answering module with VGG16 preprocessing, GoogleNews vectors, LSTM networks, scores an accuracy of 0.9721. In addition, time series analysis models such as ARIMA and adaptive models using PROPHET library for forecasting diseases, classification using random forest scoring an accuracy of 0.81, logistic regression scoring an accuracy of 0.84 for predicting diseases. The objective of this research is to compare and select the best fitting models to be used for a centralized framework for healthcare industry.Publication Open Access Development of a Virtual Learning Environment (VLE) During the COVID -19 Pandemic: A Study with special reference to Advanced Technological Institute(researchgate.net, 2022-02) Rupasinghe, L; Nowfeek, M. R. MIn this Covid-19 pandemic, Information and communication technology (ICT) plays a significant role and IT solutions such as the Virtual Learning Environments (VLE) have become vital. In pandemic situation, the Virtual Learning Environment (VLE) integrates many tools to provide higher educational institutions with an effective and efficient method to share, manage, store, and enhance their traditional approach of teaching. The implementation of VLE During the COVID -19 Pandemic has become a need at Advanced Technological institute in the learning and teaching environment. Although there is an increased willingness to its widespread implementation among higher educational institutes in this pandemic situation. The primary goal of this study is to develop a Virtual Learning Environment (VLE) for Advanced Technological Institute during the COVID -19 Pandemic, as well as to identify the existing virtual learning environment status of other higher educational institutes and implement innovative tools in Virtual Learning Environment (VLE) at Advanced Technological Institute in the COVID -19 Pandemic. The conduct of this study involved qualitative method, the finding of the research presented that existing system used only for downloading the notes of lectures. As per the finding, virtual system developed using the following features such as conducing online examination, quizzes, discussion forum and as a new components automatic question generation using natural language processing added to enhance the learning and teaching process. Adobe photoshop for image editing purpose, MySQL for creating database, Apache Web Server, WordPress, Natural Language processing (NLP) for prototype question generation and php for virtual environment development as well as Microsoft Visio for diagram drawing were utilized to develop this system development.Publication Open Access Human Tracking and Profiling for Risk Management(Global Journals, 2022-01) Ranjith, K. H. V. S; Jayasekara, A. S; Ratnasooriya, K. A. L. L; Thilini Randika, J. L; Rupasinghe, L; Liyanapathirana, CInfectious viruses are conveyed via respiratory droplets produced by an infected person when they speak, sneeze, or cough. So, to combat virus transmission, the World Health Organization (WHO) has imposed severe regulations such as mandatory face mask use and social segregation in public spaces. The ’Human Tracking and Profiling for Risk Management System (HTPRM)’ is an online application that identifies the risk associated with failing to follow proper health practices. This proposed approach, which is divided into four components, utilizes ’You Only Live Once YOLO (V3)’ to detect facemask danger, which would be determined based on two factors: wearing the face mask properly and the type of mask (Surgical, k95, homemade, and bare). The second phase is to use Open CV and SSDMobilenet to evaluate the value of a one-meter space (Social Distance) between people. The system recognizes the maximum number of individuals that can be in the vicinity of the specific hall that uses YOLO( V3) and image processing as the third procedure. In the last processing, the system identifies each person’s behavior, classifies it as uncommon or not, and calculates the risk associated with each category. Finally, the system computes the overall risk and generates a warning alarm to notify the user that they are in a dangerous scenario.