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Recent Submissions

An Analysis of Switchport Vulnerabilities and MTD Techniques Applied to Mitigate the Port Scanning Threats
(NSBM Green University, 2025-11-12) Upeksha, R.G.C.; Meegama, R.G.N.
Moving Target Defense (MTD) techniques are increasingly employed to enhance network security. MTD aims to disrupt the static nature of networks, which can facilitate attackers by easily extracting crucial network information for future vulnerability exploitation. This study investigates the potential synergy between MTD and switchport vulnerabilities. Furthermore, this paper presents a comprehensive study on Moving Target Defense and network scanning, a well-known reconnaissance technique typically involving three main phases: host detection, port discovery, and vulnerability assessment. The analysis reveals two key findings: First, the limited scope of existing MTD solutions applied to mitigate different network threats and attacks. Second, the study identifies vulnerabilities in network switches and proposed potential mitigation strategies. The systematic review analyzed twelve recent publications on MTD applications and five on switch vulnerability solutions. Moreover, the literature review suggests a promising area for further research: exploring the application of MTD techniques to mitigate the threats posed by port scanning.
A method of directly defining the inverse mapping for solutions of cauchy reaction- diffusion problems
(NSBM Green University, 2024-10-24) De Silva, B.S.K.; Dewasurendra, M.T.M.
In this paper, a semi-analytical approach called the Method of Directly Defining inverse Mapping (MDDiM) is applied to obtain an approximation series solution for the time-dependent reaction-diffusion problems. This novel approach is being applied to reaction-diffusion problems for the first time. Reaction- diffusion equations are used to describe, in modeling, nonlinear systems across various fields, including physics, chemistry, ecology, biology, and engineering. Traditionally, perturbation and asymptotic methods were widely used to obtain analytical approximations for nonlinear problems. However, these methods often failed under strong nonlinearity conditions. To overcome this obstacle, Shijun Liao introduced the Homotopy Analysis Method (HAM) in 1992, which is effective for highly nonlinear problems. However, determining the unknown functions requires computing the inverse of the linear operator. In scientific computing, calculating the inverse operator for a differential equation is a time-consuming process. To overcome this obstacle, in 2016 Liao introduced a new approach, namely the Method of Directly Defining inverse Mapping (MDDiM) with the freedom to choose the inverse linear mapping directly. The proposed method achieved a remarkable level of accuracy for solutions even with few iterations. Therefore, it can be concluded that MDDiM is not only easy to use but also accurate.
Integrative Approach to Cardiovascular Health Management
(NSBM Green University, 2024-10-24) Appuhamy, R.; Lakshan, T.; Amaraweera, O.; Jayasekara, P.; Dissanayaka, K.
Heart diseases remain a significant global health challenge, emphasizing the importance of early intervention and prevention. In addressing this issue, a mobile application was developed with four main components focusing on predicting the risk of becoming a heart patient, predicting the risk category of a heart patient, food item identification and dietary recommendation, and suggesting best rated nearby pharmacies to fulfill prescriptions. All these components include trained Artificial Intelligence models to assure a reliable performance within each component. In predicting the risk of becoming a heart patient, a trained logistic regression algorithm was used. As for predicting the risk category of a heart patient, a trained K-Nearest Neighbors algorithm was used. For the components of food item classification and suggesting best rated nearby pharmacies, YOLOv5 and TextBlob were utilized respectively. Results show that the accuracies of the trained models in the components are significant enough for mass scale utilization of all the four components of our mobile application in improving life standards of heart patients.
Source Code Vulnerability Detection Using Static Analysis and Pattern Matching
(NSBM Green University, 2024-10-24) Pitiyegedara, J.D.; Nishshanka, I.S.B.
In the rapidly evolving field of web development, ensuring the security of web applications is paramount. Most of the vulnerabilities occur due to lack of best practices during coding. Traditional security testing methods, which are predominantly reactive, tend to identify vulnerabilities in source code only after substantial portions of the application have been developed. This reactive approach significantly increases the complexity and cost of mitigating security risks. This research addresses this challenge by integrating security directly into the development process through a VS (Visual Studio) Code Extension by performing a real-time Static Analysis of source code to detect vulnerabilities which are written using JavaScript language. The tool can identify common security vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF) using the pattern matching techniques of static analysis. By integrating the tool directly into VS Code, it can provide developers with immediate feedback on security risks. The tool is not only useful for identifying vulnerabilities but also enhances the developer’s overall coding practices. The integration of the tool into the development environment will reduce time and resources spent on post- development security testing. In conclusion, this tool represents a significant step forward in the effort to embed security within the software development lifecycle. Its development and deployment will underscore the effectiveness of proactive security practices, offering a model that could be replicated and extended to other development environments and programming languages.
Enhancing Security with Biometric and Heart Rate Data Integration: A Comprehensive Review
(NSBM Green University, 2024-10-24) Perera, G.; De Silva, M.; Mandakini. S.; Kinkini, K.; Johnson, T.; Navodya, N.
Fingerprint biometrics and heart rate biometrics are foundational technologies in personal identification and authentication, each offering distinct advantages in biometric security. This review provides a comprehensive analysis of both technologies, drawing on current research to evaluate their respective strengths and limitations. Fingerprint biometrics, known for their high uniqueness and reliability, are widely used in various applications, from securing personal devices to large-scale government identification programs. However, they are not without vulnerabilities, particularly their susceptibility to spoofing and privacy concerns. On the other hand, heart rate biometrics, derived from electrocardiogram (ECG) signals, present a novel approach to authentication, offering continuous verification and a robust defense against spoofing. Despite their potential, heart rate biometrics face challenges such as accuracy issues due to physiological variations and significant privacy concerns. This paper aims to identify the gaps in current biometric security measures and propose innovative solutions for enhancing data center security by integrating heart rate data into existing fingerprint biometric systems. The proposed multi-factor authentication approach promises to enhance security by leveraging the strengths of both modalities while addressing their respective weaknesses.