AI‐Driven Adaptive Security for Sensor Networks: Next‐Generation Firewalls for Attack Detection.
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| Title: | AI‐Driven Adaptive Security for Sensor Networks: Next‐Generation Firewalls for Attack Detection. |
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| Authors: | Meegammana, Niranjan W.1 (AUTHOR) niranjan.meegammana@gmail.com, Fernando, Harinda1 (AUTHOR), Pandey, Kavita1 (AUTHOR) kavita.pandey@jiit.ac.in |
| Source: | International Journal of Distributed Sensor Networks. 7/17/2025, Vol. 2025, p1-16. 16p. |
| Subjects: | Sensor networks, Firewalls (Computer security), Artificial neural networks, Internet security, Anomaly detection (Computer security), Artificial intelligence |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Distributed Sensor Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15501329 |
| DOI: | 10.1155/dsn/5973480 |
