Pattern recognition of 5G device serial number using K-Nearest Neighbors (K-NN) machine learning algorithm
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Published:
Feb 28, 2025
Keywords:
5G Device Serial Number, K-Nearest Neighbors (K-Nn) Algorithm, Machine Learning, Pattern Recognition
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Decision Support System
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Abstract
5G networks are the latest generation of mobile communications technology that offer significant improvements in speed, capacity, and connectivity. However, along with the benefits it brings, it also brings a new set of challenges in the form of security breaches. Many 5G devices have been lost on-site. These devices are ABIA, AMIA, ASIB. Each of these devices has a serial number as identification data for each device. The rise of theft cases is due to the existence of collectors who are able to buy expensive stolen 5G devices for resale. So, the research will make the introduction of 5G device serial numbers using the Machine Learning (ML) with K-Nearest Neighbors (K-NN) algorithm. This pattern recognition is success to be done then become a guidance to recognizing stolen 5G devices. Next, this device cannot be used (deactivated) and be sold by system. This can break the demand and supply chain for stolen 5G devices. Based on the testing, there are 6 mismatches of 20 data testing or 70% data match
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How to Cite
Sitorus, Z. ., Antoni, R. ., Limbong, Y. F. ., Sitompul, J. R. . and Br Tarigan, S. M. . (2025) “Pattern recognition of 5G device serial number using K-Nearest Neighbors (K-NN) machine learning algorithm”, Jurnal Mantik, 8(4), pp. 1724-1732. doi: 10.35335/mantik.v8i4.6079.
References
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Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. In Expert Systems with Applications (Vol. 175). https://doi.org/10.1016/j.eswa.2021.114820
Choi, E., Chae, S., & Kim, J. (2019). Machine learning-based fast banknote serial number recognition using knowledge distillation and bayesian optimization. Sensors (Switzerland), 19(19). https://doi.org/10.3390/s19194218
Cuocolo, R., Caruso, M., Perillo, T., Ugga, L., & Petretta, M. (2020). Machine Learning in oncology: A clinical appraisal. In Cancer Letters (Vol. 481). https://doi.org/10.1016/j.canlet.2020.03.032
Dangi, R., Lalwani, P., Choudhary, G., You, I., & Pau, G. (2022). Study and investigation on 5g technology: A systematic review. In Sensors (Vol. 22, Issue 1). https://doi.org/10.3390/s22010026
Dong, S., & Sarem, M. (2020). DDoS Attack Detection Method Based on Improved KNN with the Degree of DDoS Attack in Software-Defined Networks. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2019.2963077
Grierson, D., Rennie, A. E. W., & Quayle, S. D. (2021). Machine Learning for Additive Manufacturing. Encyclopedia, 1(3). https://doi.org/10.3390/encyclopedia1030048
Hsu, M. M., Wu, M. H., Cheng, Y. C., & Lin, C. Y. (2022). An Efficient Industrial Product Serial Number Recognition Framework. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869266
Iavich, M., Iashvili, G., Avkurova, Z., Dorozhynskyi, S., & Fesenko, A. (2021). Machine Learning Algorithms for 5G Networks Security and the Corresponding Testing Environment. CEUR Workshop Proceedings, 3187.
Jang, W., Lee, C., Jeong, D. S., Lee, K., & Lee, E. C. (2022). Multi-Currency Integrated Serial Number Recognition Model of Images Acquired by Banknote Counters. Sensors, 22(22). https://doi.org/10.3390/s22228612
Jazyah, Y. H. (2023). 5G Security, Challenges, Solutions, and Authentication. International Journal of Advances in Soft Computing and Its Applications, 15(3). https://doi.org/10.15849/IJASCA.231130.04
Karnik, A., Adke, D., & Sathe, P. (2020). Low-Cost Compact Theft-Detection System using MPU-6050 and Blynk IoT Platform. 2020 IEEE Bombay Section Signature Conference, IBSSC 2020. https://doi.org/10.1109/IBSSC51096.2020.9332214
Kaur, J., Khan, M. A., Iftikhar, M., Imran, M., & Emad Ul Haq, Q. (2021). Machine Learning Techniques for 5G and beyond. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2021.3051557
Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188. https://doi.org/10.1016/j.comnet.2021.107840
Kuruvatti, N. P., Hernandez, R., & Schotten, H. D. (2019). Interference Aware Power Management in D2D Underlay Cellular Networks. IEEE AFRICON Conference, 2019-September. https://doi.org/10.1109/AFRICON46755.2019.9133851
Kwon, S., Park, S., Cho, H. J., Park, Y., Kim, D., & Yim, K. (2021). Towards 5G-based IoT security analysis against Vo5G eavesdropping. Computing, 103(3). https://doi.org/10.1007/s00607-020-00855-0
Lau, I., Ekpo, S., Zafar, M., Ijaz, M., & Gibson, A. (2023). Hybrid mmWave-Li-Fi 5G Architecture for Reconfigurable Variable Latency and Data Rate Communications. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3270777
Mahmood, I., Alyas, T., Abbas, S., Shahzad, T., Abbas, Q., & Ouahada, K. (2023). Intrusion Detection in 5G Cellular Network Using Machine Learning. Computer Systems Science and Engineering, 47(2). https://doi.org/10.32604/csse.2023.033842
Mustar, M. Y., Chamim, A. N. N., Putra, K. T., Nugraha, V. D. H., & Jusman, Y. (2019). Analysis and Optimization of 4G LTE Network in Jombang City East Java. Journal of Electrical Technology UMY, 3(1). https://doi.org/10.18196/jet.3148
Nurcahya, Y. A., Ismawati, E., & ... (2020). Peran Religiusitas Dan Batasan Waktu Audit Terhadap Efektivitas Fungsi Internal Audit Di Bmt Karisma Magelang. Nominal Barometer Riset ….
Praveenchandar, J., Kumar, S. V., Paul, A. C., Mukunthan, M. A., & Maharajan, K. (2023). Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices. International Journal of Interactive Mobile Technologies, 17(17). https://doi.org/10.3991/ijim.v17i17.42805
Shrestha, R., Omidkar, A., Roudi, S. A., Abbas, R., & Kim, S. (2021). Machine-learning-enabled intrusion detection system for cellular connected uav networks. Electronics (Switzerland), 10(13). https://doi.org/10.3390/electronics10131549
Storck, C. R., & Duarte-Figueiredo, F. (2020). A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated with Vehicle-to-Everything Communications by Internet of Vehicles. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3004779
Thantharate, A., Tondwalkar, A. V., Beard, C., & Kwasinski, A. (2022). ECO6G: Energy and Cost Analysis for Network Slicing Deployment in Beyond 5G Networks. Sensors, 22(22). https://doi.org/10.3390/s22228614
Wazid, M., Das, A. K., Shetty, S., Gope, P., & Rodrigues, J. J. P. C. (2021). Security in 5G-Enabled Internet of Things Communication: Issues, Challenges, and Future Research Roadmap. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2020.3047895
Xu, Z., Liu, X., Tang, J., Li, P., & Zhang, Z. (2021). Magnetic Hard Disk Serial Number Recognition Method Based on Machine Vision. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. https://doi.org/10.1109/CCDC52312.2021.9601536
Zhang, R., Zhou, W., & Hu, H. (2021). Towards 5G Security Analysis against Null Security Algorithms Used in Normal Communication. Security and Communication Networks, 2021. https://doi.org/10.1155/2021/4498324
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