IFSC-GNN: A Lightweight Graph Neural Framework for Intelligent Anomaly Detection in Cognitive Wireless Sensor Networks

Abstract

Cognitive Wireless Sensor Networks (CWSNs) play a pivotal role in dynamic spectrum access and wireless communication. However, their susceptibility to Spectrum Sensing Data Falsification (SSDF) attacks poses a severe challenge to cooperative spectrum sensing (CSS). Traditional techniques, including statistical analysis and machine learning (ML) models such as Isolation Forest (IF) and Spectral Clustering (SC), have been explored for anomaly detection. Yet, these approaches struggle with scalability and real-time responsiveness. This paper surveys the landscape of anomaly detection in CWSNs, comparing traditional and modern techniques, and proposes a new framework—IFSC-GNN—that replaces SC with lightweight Graph Neural Networks (GNNs) to enhance scalability, reduce latency, and support real-time detection on edge devices. Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations.

Introduction

Cognitive Wireless Sensor Networks (CWSNs) integrate traditional wireless sensor networks with cognitive radio technology to enable intelligent, dynamic access to unused licensed spectrum. They rely on real-time spectrum sensing, adaptive transmission, and context-awareness, making them useful in smart cities, healthcare, environmental monitoring, military communication, and industrial IoT.

In such networks, graph structures are powerful tools for representing relationships among nodes. Modern anomaly detection has shifted from handcrafted statistical methods to deep learning and Graph Neural Networks (GNNs), which efficiently handle large, high-dimensional graph data. GNNs capture complex structural and attribute-based patterns through message passing, and state-of-the-art graph anomaly detection methods combine GNNs with other deep learning techniques to improve detection accuracy.

A major security concern in CWSNs is Spectrum Sensing Data Falsification (SSDF) attacks, where malicious nodes intentionally send false sensing data during cooperative spectrum sensing (CSS). CSS improves detection reliability by combining sensing reports from multiple sensor nodes, but is vulnerable to attackers such as Always-Yes, Always-No, Random, Intermittent, and Coordinated attacks.

Traditional anomaly detection frameworks like IFSC (Isolation Forest + Spectral Clustering) work well at small scale but face severe limitations, including high computational cost (O(n³)), poor scalability beyond 5,000 nodes, and difficulty running on resource-limited edge devices.

To overcome these issues, the proposed IFSC-GNN framework replaces Spectral Clustering with lightweight GNN models (e.g., SGC, GNN-Lite) while retaining Isolation Forest for anomaly scoring. This hybrid approach improves scalability, reduces latency by about 40% in large graphs (10k+ nodes), and supports efficient deployment on edge hardware. By leveraging GNNs’ ability to model relational information, IFSC-GNN enhances detection accuracy, efficiency, and adaptability for large CWSNs.

The literature review highlights advancements in WSN and CRN security, machine learning and deep learning for anomaly detection, GNN-based spatial–temporal modeling, federated learning for privacy, and efficient distributed detection methods—underscoring the growing importance of advanced graph learning techniques for securing cognitive networks.

Conclusion

Cognitive Wireless Sensor Networks (CWSNs) represent the next generation of intelligent spectrum-aware communication systems, but remain highly vulnerable to Spectrum Sensing Data Falsification (SSDF) attacks. Traditional anomaly detection methods, including statistical models, classical machine learning, and clustering algorithms, have laid foundational defenses but often suffer from scalability issues, high latency, or dependency on labeled data. This review provided a detailed comparison of these approaches, highlighting the strengths and limitations of techniques like Isolation Forest and Spectral Clustering, which together form the IFSC framework. While IFSC demonstrates the power of unsupervised hybrid models, its reliance on Spectral Clustering hinders its scalability and responsiveness, especially in large-scale, real-time environments. To address these shortcomings, we proposed a conceptual framework, IFSC-GNN, which replaces Spectral Clustering with lightweight Graph Neural Networks (GNNs). These models, including SGC, TinyGNN, and GNN-Lite, offer structural awareness, reduced latency, and compatibility with edge computing devices. Experimental simulations referenced in the literature suggest that GNN-based models can reduce detection latency by up to 40% in networks with over 10,000 nodes. While IFSC-GNN presents a promising path forward, several open challenges remain, including the need for real-world datasets, interpretability of GNNs, energy-efficient deployment, and robustness to adversarial attacks. Future work must explore federated learning, AutoML, and secure GNN variants to further enhance CWSN resilience. In summary, IFSC-GNN offers a scalable, real-time, and intelligent alternative to traditional SSDF defense strategies—paving the way for more secure and autonomous cognitive sensor networks.

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Copyright

Copyright © 2025 Ms. Rashee Kori, Ms. Megha Soni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.