Theses
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Postgraduate students are required to submit a thesis as part of fulfilling the requirements of their respective postgraduate degree programmes. This community features merit-based graduate theses submitted by SLIIT postgraduate students. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a
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Publication Open Access Enabling Consistent Stateful Security in Distributed Web Application Firewalls: A Framework for Scalable Cloud Environment(Sri Lanka Institute of Information Technology, 2025-12) Palendrarajah, PThe rapid adoption of cloud-native infrastructures has highlighted a critical limitation in existing Web Application Firewalls (WAFs): their stateless design restricts consistent enforcement of security policies across distributed environments. This research addresses this gap by designing and evaluating a portable persistence module for open-source WAFs, enabling stateful security enforcement through integration with distributed data stores. Guided by the principles of design science research [1], the study develops a pluggable framework that supports both Redis and Memcached as backends. Redis is widely recognized for its durability and advanced data structures [2], while Memcached offers lightweight, in-memory caching optimized for speed [3]. By embedding the module within ModSecurity v3 [4] and deploying it on AWS cloud infrastructure, the research benchmarks the comparative performance of Redis and Memcached under simulated traffic and attack scenarios, including Distributed Denial of Service (DDoS) conditions [5]. Evaluation metrics include latency overhead, throughput, memory utilization, and resilience under node failures. Preliminary results indicate that Redis achieves superior consistency and resilience, albeit with higher memory consumption, while Memcached provides lower latency at the cost of weaker fault tolerance. Beyond technical performance, the research contributes a generalizable, portable framework that can be embedded into other open-source WAFs, expanding their applicability in distributed and multi-tenant environments. Both artifact and empirical evaluation contributions positions the work as a step forward in bridging distributed systems and web security, while also providing a foundation for future enhancements such as adaptive, machine-learning-based intrusion prevention [6].Publication Open Access AI-DRIVEN SELF-HEALING TEST AUTOMATION FOR ENTERPRISE SOFTWARE SYSTEMS(Sri Lanka Institute of Information Technology, 2025) Jinarathna, H. D. R. J.Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms are backbone tools for today’s businesses, helping teams around the company work together efficiently. But because these systems are huge and always changing, testing them gets tricky. Methods we’ve traditionally used—whether having testers run scripts by hand or running automated scripts—can’t keep up. To solve these problems, I’ve built a test automation framework that uses AI to repair itself as it runs. By incorporating Natural Language Processing (NLP) for spotting system changes and Reinforcement Learning (RL) for teaching tests to heal and get sharper, the framework learns to find when a test has broken, to fix the code on the fly, and to keep fine-tuning itself, so people hardly need to step in. I shaped the system by talking to QA engineers about the roadblocks and running pilot cases in a pretend ERP setup. Those conversations, plus the numbers, helped us tweak the design so it feels less like a lab gadget and more like a teammate. Early results are encouraging—flake tests bounce back 35% more often and testers spend 25% less time rewriting logic by hand. My research helps Software Quality Assurance Engineers, learners, and software businesses by offering an easy-to-understand, adaptable way to test big-company software. The results show how using smart technology can make software testing faster, cheaper, and better.Publication Open Access Design and Validation of an AI-Enhanced Career Guidance Framework for Sri Lankan Secondary Education(Sri Lanka Institute of Information Technology, 2025-12) Karthiha, S.Career guidance is a decisive factor that contributes to the educational and professional path of students, but in Sri Lanka, the practices that are currently being used are mostly manual, fragmented, and unfair. This paper forms a conceptual model of an Artificial Intelligence (AI)-driven career guidance system that provides students with personalized educational and professional advice to high school students. The study is based on a mixed-methods research framework that combines both quantitative data collected in the form of the survey of 379 educational professionals working in nine provinces and the qualitative information gathered in the form of the interviews with ten experts. Key determinants of career decision-making were identified in a quantitative analysis and found to include academic performance, aptitude, personal interests, and socio-economic background, and six interrelated dimensions were identified in a qualitative thematic analysis: human-dominated guidance, family and cultural influence, perceived fairness of AI systems, ethical and infrastructural barriers, hybrid human-AI collaboration, and equity of access. The presented framework integrates these insights into a hybrid structure, which will combine AI-based analytics with advisory and parental judgment and will be culturally sensitive and ethically valid. The model is more accurate, inclusive, and efficient because it matches student profiles with the trends of the labour-market. The results prove that AI is capable of being more of a supplement to the human knowledge and can be used to increase access to data-driven counselling in even schools with scarce resources. The research has theoretical value in that it links AI technology to the career development theory and practical in that it will provide a replicable solution to the modernization of school guidance ecosystem in Sri Lanka by policymakers and educators. It ends with implementation recommendations on data ethics, transparency and capacity building to attain equitable and evidence based educational and career decisions support.Publication Open Access AI-Driven Help Desk Integration: Enhancing Customer Support with Chatbots, Sentiment Analysis, and SLA Automation(Sri Lanka Institute of Information Technology, 2025-11) Nimnadi DilsikaThis research investigates the integration of Artificial Intelligence (AI) into help desk systems to improve customer service efficiency, accuracy, and overall satisfaction. The proposed AI-driven help desk framework combines three intelligent components: a chatbot for instant and automated responses, a sentiment analysis engine to detect and interpret customer emotions, and a Service Level Agreement (SLA) management module that ensures real-time tracking of response and resolution performance. Using a dataset of 40,000 simulated support tickets, the system was evaluated for key metrics such as response time, SLA compliance, and customer satisfaction levels. The results demonstrated notable improvements, including faster response rates, higher SLA adherence, and enhanced emotional understanding in customer interactions. Overall, the study confirms that AI integration transforms traditional help desks into proactive, data-driven, and emotionally intelligent service environments. Future advancements will focus on predictive SLA modeling, multilingual capabilities, and multimodal sentiment analysis for broader adaptability.Publication Open Access Development of a Neural Network-Based Framework for Skin Disease Recognition(SLIIT, 2024-12) Senadhipathi, L.A.N.MSkin diseases impact humans, animals, and plants and are typically brought on by germs or infections. These ailments include ringworm, yeast infections, brown sports, allergies, and other conditions. Early detection can help lessen the impact of diseases. But there are other risks that the skin can encounter, one of which is illness. Fungi, bacteria, allergens, enzymes, and viruses are the main causes of skin problems. Skin conditions impair not just one's physical health but also their psychological well-being, especially in those who have damaged or even scarred skin. Identifying the condition via manual feature extractions or symptom-based approaches requires time and requires comprehensive data for accurate identification. Serious health concerns are associated with skin diseases, which require an accurate and timely diagnosis for appropriate treatment. In particular, convolutional neural networks (CNNs) have shown promising results in automated skin disease identification recently. In this study, A novel CNN-based approach is presented, achieving a 95% accuracy rate in classifying seven different types of skin diseases from the HAM10000 image dataset. Dermatoscopic images from the HAM10000 dataset are preprocessed and categorized into seven classes: basal cell carcinoma, melanoma, vascular lesions, dermatofibroma, melanocytic nevi, and benign keratosis. After extensive testing and fine-tuning, it achieved an overall accuracy of 95% on the testing set. The outcomes show that the suggested CNN-based method can accurately identify a variety of skin conditions by using the HAM10000 picture dataset. Deep learning techniques can significantly help dermatologists and other healthcare professionals diagnose skin conditions accurately and automatically, enabling them to provide prompt and efficient treatments. This work adds a great deal to the growing field of dermatological computer-aided diagnosis and offers valuable data for upcoming advancements in the identification of skin diseases.Publication Embargo Mitre attack framework adoption as a siem rule base using machine learning approach(2021) Weeraman, P.W.R.S.Digital transformation is the standard business strategy approach in most Organizations. Every person is looking for digital solutions to aid their routine works. Every Organization looking possibility move to physical office concept for virtual office concept. Even homemakers and bargain hunters also expect to move online shopping with doorstep delivery solutions with this COVID-19 pandemic. Every business needs to adopt IT functions for their business process to ensure business stability or increase their revenue. Most large-scale enterprises have a dedicated IT strategy approach to align with their business strategy. They follow best IT security practices such as SIEM, security operation centers (SOC), annual IT compliance review, IT audit and best security devices in the market. However, most of the business do IT system adoption without a preplanned process. They do not follow any best it practices in term of IT security. Further, they do not have a proper IT strategy that aligns with business objectives. Most small and medium scale business with minimum IT infrastructures and IT operations. The absence of a proper IT security approach in the business may introduce new IT risk to their information and business. This Research makes experimental approach to adopt cyber threat intelligence to SIEM detection base using adversary tactic, technique, procedure (TTP) and machine learning (ML) instead of signature-based detection methods. TTP change is relatively more challenging than IP address or file hash change. This research concern uses TTP-based Security information and event management systems (SIEM) solution using open-source software and MITRE ATT&CK community framework. Further, this Research aims to reduce operating expenses and capital expenses using a community-based framework and opensource software.