# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 1 Introduction
Afterwards, a large body of literature is dedicated to the general issues of design, implementation and identification algorithm relevant to many kinds of RF identification systems [23; 24; 25; 26]. A comprehensive overview of high-level issues in the context of device identification method via RF fingerprint is summarized by [PERSON] et al. [15]. That is why, in this paper, a device identification method is proposed based on the IoRL framework which is a typical indoor solution that integrates VLC-based communication, positioning and illumination systems within the 5G network [27; 28]. This proposal aims to improve the security for the VLC-based 5G network under the public areas and broadcasting scenario as mentioned above. In this approach, fingerprints are extracted from the signal emitted by Light-Emitting Diodes (LEDs). These fingerprints are unique for every LED even if they are of the same type. Moreover, by employing a machine-learning-based classifier, the system is able to distinguish these fingerprints, thereby identifying with which device it is communicating. This fundamentally eliminates the threat of rouge base-station. It replaces the traditional encryption by a real-time and OTP (One Time Pad) encryption, which strengthens the overall security of the 5G networks. Moreover, the only prerequisites of this method are the received analog signal and some software modules, which means that this mechanism can be integrated into an existing 5G network physical layer. Considering the above, the main contributions of this paper can be summarized as follows:  
* We introduce the LED fingerprint model based on the characteristics of the LED equivalent circuit and design the LED fingerprint extraction and identification mechanisms--by fitting the power spectrum. The parameters which represent the LED's inherent and stable nature are chosen to constitute the LED feature vector. The feature vector of each LED forms its fingerprint. A multi-SVM (Support Vector Machine) classifier is investigated theoretically to illustrate the process of the fingerprint identification. * We illustrate the conceptual design of a typical 5G VLC multi-access scenario using the proposed security solution. We present how LED fingerprinting could be used in real-life systems. To detail the process, we choose the IoRL project as an exemplary test system. * We demonstrate the feasibility and accuracy of this method in a practical indoor VLC-based 5G network. During experimental evaluation, four identical LEDs were used to extract their fingerprints from the emitted 5G NR signals. Four machine-learning-based classifiers, i.e., decision tree, Naive Bayes, SVM (Support Vector Machine), and KNN (K-Nearest-Neighbor) were employed to identify the extracted LED fingerprints. It turned out that the best results were achieved for the SVM classifier, which reached the accuracy of 97.1%. The rest of this paper is organized as follows. Section 2 describes the proposed LED fingerprinting method in detail. Then, the experimental methodology and obtained results are detailed in Section 3. Finally, Section 4 concludes our work and outlines potential future directions.