Faculty of Computing Scopus 2

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Author: search.filters.author.Fernando, H

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    PublicationOpen Access
    AI-Driven Adaptive Security for Sensor Networks: Next-Generation Firewalls for Attack Detection
    (John Wiley and Sons, 2025-07-25) Meegammana, N.W; Fernando, H
    Sensor networks are increasingly critical in modern smart environments; however, their limited computational resources expose them to sophisticated cyber threats. Traditional static firewalls and computationally intensive deep learning models are impractical for securing such networks. This study proposes an adaptive next-generation firewall (NGFW) that dynamically switches between shallow and deep AI models based on real-time network load and resource availability. Four neural network models were trained using 20 and 40-feature subsets of the UNSW-NB15 dataset. Two runtime strategies (i) on-demand model loading and (ii) preloaded model switching were developed and evaluated through simulation under real-time conditions. Experimental results indicate that the preloaded approach achieves up to 96% accuracy, 98% precision, and 4-ms inference latency, with a memory footprint of 19 MB, outperforming static AI firewalls in both efficiency and scalability. The proposed NGFW framework offers a resilient and scalable solution for real-time attack detection in resource-constrained environments without requiring frequent model retraining. Future enhancements include hybrid shallow–deep model architectures, continuous federated learning for decentralized adaptability, and the integration of explainable AI to enhance transparency and trustworthiness in edge security deployments.
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    PublicationOpen Access
    Real Time Accident Detection and Emergency Response Using Drones, Machine Learning and LoRa Communication
    (Science and Information Organization, 2025) Bandara H.M.S.I.D; Maduhansa H.K.T.P; Jayasinghe S.S; Samararathna A.K.S.R; Fernando, H; Lokuliyana, S
    Road accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely detection and efficient traffic management are critical to reducing the impact of such events. This study proposes a real time Accident Detection and Emergency Response System with integrating Machine Learning IoT enabled drones and LoRa communication. The system combines real time accident detection using CCTV, drone assisted fire detection for post accident scenarios, crime activity monitoring and automated traffic management to reduce congestion and improve public safety. LoRa ensure long range, energy-efficient communication. ML models improve detection accuracy across accidents, fires, crimes and vehicles. Figures and sensor data are analyzed in real time to trigger alerts and assist emergency responders. The system supports scalable integration with existing urban infrastructure, promoting the development of smart city safety frameworks. By minimizing emergency response time, limiting secondary incidents and improving situational awareness, the proposed solution addresses critical gaps in current urban safety systems. It offers a practical, intelligent and adaptive approach to accident mitigation and traffic control in smart cities.