http://dlib.iit.ac.lk/xmlui/handle/123456789/2802 Tue, 07 Apr 2026 13:58:12 GMT 2026-04-07T13:58:12Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2873 Zero Trust Architecture Framework To Secure & Mitigate Organizational Threats Kuruppu, Tiran Zero Trust has been very hot topic within the industry, specifically because the industry is changing rapidly and there is a lot more remote working happening, so zero trust Architecture is a policy where it’s very important to give least privilege access to all users out there, so verifying all the users, applications & devices on the network before allowing them to any critical assets in organizations. This is becoming increasingly important because of network boundaries have been changing and people are connecting the organization network from various locations, users and applications are spanning multiple products and services across multiple different locations and this why it makes it more and more important to focus on zero trust. Zero trust architecture (ZTA) processes every request and gives the subject a resource without depending on implicit confidence, in contrast to perimeter-based architecture, which assumes that any subject inside the wall (i.e., inside the pre-defined perimeter) is trustworthy. This document introduces the ZT and ZTA concepts based on NIST Special Publication SP800 207. Also, the difficulties, procedures, and factors to think about when switching from the old architecture to ZTA are given and discussed. Mon, 01 Jan 2024 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/2873 2024-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2872 Cyberbullying Detection Based on Sinhala Language Context on Social Media Peiris, Sarani "Cyberbullying has become a pervasive issue on social networking sites, leading to severe negative impacts on victims. Despite its prevalence, a comprehensive technological solution to address this global problem remains elusive. In response, this study proposes an automated approach to identify cyberbullying in Sinhala language text, including emojis, using a deep learning approach. The primary objective is to provide a means for relevant parties to take preemptive action against cyberbullying instances before they escalate. Utilizing a Convolutional Neural Network (CNN), the technical solution involves training the model on a dataset consisting of Sinhala language text extracted from social media platforms. The CNN architecture comprises multiple layers, including convolutional and pooling layers, designed to extract relevant features from the text data and classify it based on hate level. Evaluation of the proposed approach yielded promising results, with the CNN achieving an accuracy rate of 79%. This performance metric indicates the model's effectiveness in accurately identifying instances of cyberbullying in Sinhala language text, thereby offering a potential solution to mitigate its harmful effects in society." Mon, 01 Jan 2024 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/2872 2024-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2871 Enhanced Online Payment Fraud Detection Using Hybrid Machine Learning Mechanism Gunarathna, Sachitha "The rush in digital transactions has underscored the importance to develop accurate and trustworthy mechanism for detecting online payment fraud. In response to these challenges, author propose an innovative hybrid machine learning approach. The methodology integrates different algorithms to enhance the accuracy and resilience of fraud detection systems. The process initiates with the curation and preprocessing of a comprehensive dataset containing both genuine and fraudulent transaction data. Subsequent feature engineering extracts crucial insights from raw data, augmenting the model's discerning capacity. Random Forest classifier, Gradient Boosting classifier, AdaBoost classifier, and K-Nearest Neighbors classifier (KNN classifier) were judiciously chosen to develop an ensemble of model as an output of the research. The main point of the methodology lies in the ensemble model creation phase, where predictions from individual models merge through voting mechanism leveraging the unique strengths of each classifier algorithm. This merging results in a more accurate and adaptable fraud detection system. Critical cross-validation and hyperparameter tuning are used to meticulously refine the ensemble model, ensuring optimal performance without overfitting. The ensemble model demonstrated superior performance, significantly improving fraud detection rates while reducing false positives, thereby enhancing user experience. In summary, the proposed hybrid machine learning approach offers a strong and precise solution for online payment fraud detection. By using the strengths of various classifier algorithms, it strengthened accuracy and establishing a robust defence against the evolving landscape of digital payment fraud strategies. Subject Descriptors: • Computing methodologies → Artificial intelligence → Machine learning → Hybrid learning approaches • Applied computing → E-commerce → Security and privacy in e-commerce → Fraud detection Keywords: Machine learning, K-Nearest Neighbors classifier, Random Forest, Logistic Regression, Ensemble model, Voting, Cross-validation, Hyperparameter tuning" Mon, 01 Jan 2024 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/2871 2024-01-01T00:00:00Z http://dlib.iit.ac.lk/xmlui/handle/123456789/2870 Increasing the Efficiency of Security Operation Centers Using Automations Senanayake, Ruvimal "Organisations need SOCs to combat the growing volume and complexity of cyber threats. Data overload, alert weariness, and rapid response might reduce SOC efficiency. This study investigates SOC performance to address inefficiency. An in-depth research shows that manual processes, limited scalability, and the inability to prioritise critical signals impair SOC threat identification and mitigation. The recommended intentional SOC workflow automation to address these challenges. We streamline tasks, analyse data, and respond to incidents in real time using AI, machine learning, and robotic process automation. The proposed automated method frees SOC analysts to focus on strategic decision-making and threat hunting. Our automation solutions are tested in case studies and simulations to improve SOC efficiency." Mon, 01 Jan 2024 00:00:00 GMT http://dlib.iit.ac.lk/xmlui/handle/123456789/2870 2024-01-01T00:00:00Z