Faculty of Computing
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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 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 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.Publication Open Access Androsafe: Online malware analysis with static and dynamic methods(Annual Technical Conference 2016 - IET- Sri Lanka Network, 2016) Kesavan, K; Liyanapathirana, C; Sampath, S. A. W. S; Sureni, Y. M; Koshila, C. P; Wanigarathna, S; Nawarathna, C. P; Rupasinghe, LWith an estimated market share of 70% to 80%, Android as becoming the most popular operating system for smartphone and tablet. Cyber criminals naturally expanded their various activities towards Google’s mobile platform.An additional incentive for mobile malware authors to target Android instead of another mobile platform is Android open design that allows users to install the application from a variety of sources. "Androsafe" is an online malware analysis tool which can analyze malware in an isolated environment without any damaging to the mobile device by using both existing and new anomaly based and behavioral analysis. Through this combination, we can analyze a large number of malware families because some malware families may only perform signature base or behavioral. Then the sandboxes based on signature will not have analysis malware families that only perform a behavior and the sandboxes based on behavior will not analysis signaturebased malware families.“Androsafe” sandbox will be hosted in the Honeynet Project’s cloud. Dynamic Analysis will be queued and run in the background, and an email which contains malware analyzing report will be sent to the user when the analysis is over. This method is very efficient more than offline kernel and app base sandbox.Publication Embargo Enhancing the security of OLSR protocol using reinforcement learning(IEEE, 2017-09-14) Priyadarshani, H; Jayasekara, N; Chathuranga, L; Kesavan, K; Nawarathna, C; Sampath, K. K; Liyanapathirana, C; Rupasinghe, LMobile ad-hoc networks are used in various institutions such as the military, hospitals, and various businesses. Due to their dynamic mobile structure-free and self-adaptive nature, they are ideal to be used in emergency situations where the resources available are limited. The wireless range of the devices in the MANET is narrow. In order to communicate with the desired device often times it is necessary to use intermediate devices between the source and the destination. Therefore, it is important to secure sensitive information sent through intermediate devices. OLSR is a widely used MANET routing protocol. Although OLSR protocol has excelled in performance and reliability, it is rather poor in security. In this context, we attempt to improve the security of OLSR protocol with the aid of Q-Learning by selecting trustworthy nodes to forward messages. Behavior of the nodes is used to determine the trust of the nodes.