Ruvan Abeysekara

Work place: IIC University of Technology, Faculty of Graduate Studies, Phnom Penh 121206, Cambodia



Research Interests: Data Mining


Ruvan Abesekara obtained his Ph.D. in Computer Science and Technology from Dalian Maritime University, China, and holds an MSc in Computer Science from the University of Colombo School of Computing. He is currently a professor in Computer Science and the Vice Chancellor of the British College of Applied Studies, Sri Lanka. He also serves as an adjunct faculty member at the IIC University of Technology, Cambodia. In addition to his academic qualifications, Ruvan holds several industry and professional certifications. He is a member of several organizations such as The Institute of Doctors, Engineers and Scientists, the Institution of Engineering and Technology, the Institute of Electrical and Electronics Engineers, the British Computer Society, the Chartered Institute for IT, the Computer Society of Sri Lanka, and the Australian Computer Society, among others. Furthermore, he is a Microsoft Certified Peer Coach and a Cisco Certified Instructor. His current research interests include IoT, AI for cybersecurity, data mining and algorithms, and privacy-preserving techniques.

Author Articles
A Novel Framework for Real-Time IP Reputation Validation Using Artificial Intelligence

By NW Chanaka Lasantha Ruvan Abeysekara M.W.P Maduranga

DOI:, Pub. Date: 8 Apr. 2024

This research paper introduces and discusses deeply an approach to the real-time IP reputation (IPR) concept and its validation process for an Amazon Web Services Web Application Firewall (AWS WAF) backend application safeguarding using intelligence (AI) technologies. Also, the study examines existing IP reputation solutions over AWS WAF which Evaluates methodologies highlighting the difficulties faced and real-world challenges in validating IPR while utilizing OpenAI’s generative AI language models the framework aims to automate the extraction and interpretation of IP-related information from AWS S3 real-time log storage sources such as logs, and natural language reports based on JSON structure. These dedicated algorithms developed, and AI model concepts are powered by processing language enabling them to identify incidents and detect patterns of IP behavior that should indicate security risks. Also, models do not directly access databases, as they can analyze data from APIs featured and with local maintenance database such that AbuseIPDB to evaluate the reputation of IP addresses Integrating AI into the process of validating IPs can greatly improve cybersecurity operations by summarizing findings and providing insights ultimately saving time and resources.

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