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 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 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 Development of Cyber Threat Intelligence System in a SOC Environment for Real Time Environment(Department of Computing and Information Systems, Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka, 2021-02-24) Varatharaj, A; Rupasinghe, P. L; Liyanapathirana, CNow a days, Information Communication Technology (ICT) plays an important role in the world. In IT, Cyber Security holds a vast place. Cyber Threat Intelligence (CTI) leads the significant place within Cyber Security, as many Cyber Threats need to be faced every day by a particular organization. Security Operation Center (SOC) helps to monitor and analyze an organization’s security position in Real Time. This paper proposes about the Cyber Threat Intelligence framework in a SOC Environment in Real Time. The proposed framework contains of three layers, which are built above Security Onion. The Layer 1 comprises of input data from online and offline sources. In Layer 2, implemented two components namely Filter data and Cut down data, which receive the data from Layer 1. Finally, in Layer 3 delivers a detailed report. As the input for the Layer 1, Financial Datasets is used. These Financial Datasets, which helps in order to detect the Financial Frauds. Machine Learning is used to train the model. By implementing CTI System in an organization, it helps to gain predictive output regarding the upcoming threats. Also, it helps to ensure the reputation of an organization by establishing trust between the users. Helps to increase the number of customers to an organization. The above are the advantages gained by a particular organization by having a CTI System.Publication Open Access Intelligent Cyber Safe Framework for Children(IEEE, 2021-12-01) Harfath, M; Amrith, R; Dulanaka, N; Perera, p; Rupersinga, L; Liyanapathirana, CTechnology-wise, children are much ahead of their parents. Due to hectic schedules and daily struggles, time is limited for parents. For that reason, the AI-powered child protection system helps protect children from modern cyber-attacks while offering parents more control over their children. Keyloggers, keystroke and mouse movement loggers help to collect data and can record user behaviour and find patterns. Furthermore, the use of those records is able to detect children’s improper behaviour and reveal children’s emotional states. Behavioral Data Extractor and Risk Analysis systems can analyze huge numbers of URLs and web content recorded by proxy, as well as application usage and screen times collected by background service. The Smart Resource Restricter is designed to help parents and children navigate the web safely and appropriately. The research can identify and prevent child predators. Indeed, cyberbullying and phishing attacks cross many boundaries, causing great harm to the community. It blocks outside threats and notifies parents of sexual and other online predators that often target children. The PandaGuardian successfully achieved its goal with the assistance of different algorithms and the respective outcomes. The model evaluation report, which compares all the methods, is a guardian companion. Parents could get assistance in order to safeguard their children from the day-to-day evolving cyber threats.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.Publication Embargo An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(IEEE, 2020-12-10) Jayaweera, K. N; Kallora, K. M. C; Subasinghe, N. A. C. K; Rupasinghe, L; Liyanapathirana, CAccording to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Embargo Vision Based Intelligent Shelf-Management System(IEEE, 2021-12-01) Priyanwada, H. A. M; Madhushan, K. A. D. D; Liyanapathirana, C; Rupasinghe, LCurrently supermarkets are more popular, and the local stores are leaving the competition. when people go to supermarkets, they find various items stocked on seemingly unlimited shelves. Supermarket shelves needed to be filled with the items accordingly. The most common problems in the supermarkets are identifying the empty shelves, on-shelf availability, and future sales. The labors cannot always track the empty shelves and on shelf availability levels due to their workloads. Moreover, it is a time-consuming method for the labors which can affect the customer satisfaction and business profit. Every month, supermarkets buy the required number of products from related manufacturing companies by analyzing the previously purchased products and their sales. This is usually done manually by managing excel sheets which is also time consuming and not reliable. Especially during the seasonal times or pandemic situations they cannot use the manual method which must also be done as fast as possible. Therefore, this system can be used to assist in empty shelf detection, percentage of on-shelf availability and in the prediction of future sales. The implementation of on-shelves percentage detection service is done using machine learning. Machine learning processes are carried out for implementing the necessary functionalities and algorithms. Initially, the camera captures clear and real time images regularly. Then the system processes and detects the image similar to the threshold percentage or detect the empty shelves. When the system detects the threshold percentage or empty shelves, the system will provide an alert to the labors. The Implementation of the predicting the future supply and demands is done using time series analysis using several existing machine learning algorithms by utilizing historical data. In this research the prediction of future sales and demand in the supermarkets is done by considering the customers' behavior, the variety of product groups they buy and seasonal changes. These predictions are made on the assumption of a constant per capital supply of products and demand in our system.