Light Weight CSI-Based Physical Layer Authentication Model for IoT Networks

This paper introduces a novel physical layer authentication technique for Internet of Things (IoT) networks, leveraging channel state information (CSI) data from Wi-Fi signals to distinguish between authorised and unauthorised nodes and thereby enhancing security without compromising performance. The core novelty lies in its integrated framework, which employs non-negative matrix factorization (NMF) for efficient feature selection and a Gaussian mixture model (GMM) for identifying complex patterns within the CSI data specifically adapted to the dynamic nature of IoT networks. NMF is utilised to mitigate the high dimensionality and redundancy inherent in raw CSI metrics, reducing processing load, extracting salient features, alleviating overfitting risks, and exhibiting superior resilience to noise. Following NMF, the GMM component is used for data classification, capitalising on its probabilistic and soft clustering attributes to represent intricate distributions and handle heterogeneous CSI data characteristics. This integrated proposed methodology not only exploits the inherent nonlinear and probabilistic characteristics of CSI data but also upholds computational efficiency, making it highly suitable for implementation in resource-constrained IoT wireless networks. The model achieves exceptional classification proficiency, with an accuracy rate of 99.83 percent and a recall of 100 percent, which are crucial for cybersecurity and anomaly detection. Furthermore, the system is designed for efficiency and minimal resource consumption, exhibiting good computational efficiency, reduced training duration, and lower energy consumption compared with more complex, heavily exploited architectures for CSI data processing like CNN and CNN + LSTM, making it particularly suitable for resource-constrained IoT environments.