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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Publication Open Access A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis(PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, DIn an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.Publication Embargo Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms(IEEE, 2022-10-04) Nalmi, R; Rathnayake, N; Mampitiya, L.IIn the domain of autonomous underwater vehicles, the classification of objects underwater is critical. The hazy effect of the medium causes this obstacle, and these effects are directed by the dissolved particles that lead to the reflecting and scattering of light during the formation process of the image. This paper mainly focuses on exploring the best possible image classifier for the underwater images of the different fish species. The classifications were carried out by different hybrid and supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). This study compares the algorithms’ accuracy and time and analyzes crucial features to decide the most optimal algorithm. Furthermore, the results of this paper depict that using dimension reduction methods such as PCA and LDA increases the accuracy of some algorithms. Random Forest was able to outperforms with a higher accuracy of 99.89% with the proposed feature extraction methods.Publication Embargo Sustainable tourism: Application of optimization algorithms to schedule tour plans(IEEE, 2019-01-31) Perera, D; Rathnayaka, C; Siriweera, L; Dilan, S; Rankothge, WOne of the challenging problems in the tourism industry is to maintain the environmental sustainability of the tourists attracted locations while giving a better user experience for the tourists. The proposed platform for sustainable tourism management system consist with following modules: A prediction module to predict an approximate value on tourist arrival for each location, an optimization algorithm module to decide the number of tourists that can be accommodated in each location considering the environmental sustainability, and an optimal path generating module to show the best route to each location. The optimization algorithm module is developed to decide the number of tourists for each location based on two approaches: Genetic Algorithms and Iterated Local Search. Next the optimal path generating module is developed based on traveling salesman problem.In this paper, the performances of the optimization algorithm module and the optimal path generating module is presented. Results show that, using the suggestions given by the algorithms help the tourist to enjoy a better experience in travelling while ensuring the sustainability in the tourism industry.Publication Open Access Knowledge Discovery with Data Mining for Predicting Students’ Success Factors in Tertiary Education System in Sri Lanka(University of Moratuwa, Sri Lanka, 2017-10-31) Kasthuriarachchi, K. T. S; Liyanage, S. RKnowledge discovery in educational data would be so basic to determine better expectations on the undergraduates. Distinguishing proof of the components influence to the execution of undergraduates in light of various attributes will be supportive for instructors, educators and managers viewpoints. This paper endeavors to utilize different data mining ways to deal with find forecast manages in undergraduates’ data to distinguish the components influence to the scholarly accomplishment in their tertiary education. The approach of this exploration observed the aftereffects of three mining algorithms with about 3800 undergraduates’ records and the calculation which demonstrated the most elevated exactness has chosen as the best model and the connections acquired through that were gotten to foresee various elements against the objective of whether they will get the degree or not following three years of the university life. Naïve Bayes, Decision Tree and Support Vector Machine were used in predicting the most affecting factors to the performance of students. According to the prediction accuracy levels, the results of Decision Tree were selected since it outperforms the rest for the selected data set. Finally, the results were evaluated using a correlation analysis to select the most prominent factor. According to the test, the age, past failure modules, performance of past semesters were selected as the most influencing factors to the success or failure of the students in tertiary education system in Sri Lanka.Publication Embargo Intelligent Timetable Scheduler: A Comparison of Genetic, Graph Coloring, Heuristic and Iterated Local Search Algorithms(IEEE, 2019-12-05) Ekanayake, T. W; Subasinghe, P; Ragel, S; Gamage, A; Attanayaka, SA Timetable scheduling is a monotonous task and a problem in an educational institute. This is because many rules and constraints are involved, which can be categorized as hard and soft constraints. Mainly, a university must produce two types of timetables, which are examination, and semester timetables. This paper has reviewed the Exam Timetabling problem with Genetic and Graph Coloring algorithms and the Semester Timetabling problem with Heuristic and Iterated Local Search algorithms. Our aim here is to develop a possible and correct solution for each timetabling problem using the above-mentioned four different approaches.Publication Embargo WoKnack – A Professional Social Media Platform for Women Using Machine Learning Approach(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Shanmugarajah, S.; Praisoody, A.; Rakib Uddin, M.D.Today’s generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system.Publication Open Access Cluster density of dependent thinning distributed clustering class of algorithms in ad hoc deployed wireless networks(Hindawi, 2012-01-01) Gamwarige, S; Kulasekere, E. CDistributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node’s decision to be a CH depends on the decision of its neighbors. An analytical technique to determine the cluster density of this class of algorithms is proposed. This information is required to set the algorithm parameters before a wireless network is deployed. Simulation results are presented in order to verify the analytical findings.Publication Embargo Distributed precoding for MISO interference channels with channel mean feedback: Algorithms and analysis(IEEE, 2013-06-09) Ding, M; Tirkkonen, O; Berry, R. A; Ulukus, SThis work focuses on the design and analysis of distributed stochastic precoding algorithms for multiple-input single-output (MISO) interference channels, where each transmitter is provided with mean information of its intended channel and that of interfering channels. Unlike in cases where exact channel gains are known as in most existing works, here generalrank precoding is required for optimality instead of the rank-one beamforming. An efficient algorithm for the distributed implementation of the Nash equilibrium precoding is first proposed. A sufficient condition for this algorithm to converge to the unique equilibrium is derived for the two-user case based on stochastic ordering, and is valid for a wide range of system parameters. To improve the sum-rate performance under medium to strong interference, a pricing-based algorithm is also provided and its convergence analyzed. The two algorithms are compared in terms of sum-rate and system overhead.