Exploring Multiclass Data Poisoning within an Industrial 5G Private Network

Machine Learning (ML) models have proven effective in optimizing wireless and private networks. However, recent research highlights the threat of data poisoning attacks on ML models. To analyze such a threat on an industrial 5G private network, this work investigates the effectiveness of data poisoning against it. We primarily focus on poisoning four ML models: Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), at three poisoning levels: 10%, 15%, and 20%. Our research shows that all models introduce instability in the network, rather than optimization, whereas neural networks are less affected by data poisoning compared to other models. At the 20% data poisoning level, model performance degrades by about 6-7% for SVM, RF, and DT, while ANN shows a minimal disruption of 2%.

Exploring Multiclass Data Poisoning within an Industrial 5G Private Network

Anum Paracha, Birmingham City University, UK; Oluwatobi Baiyekusi, Birmingham City University; Junaid Arshad, Birmingham City University, UK; De Mi, Birmingham City University; Chen Lu, Shenzhen Institute of Information Technology; Yunsheng Zhang, School of International Exchange and Cooperation; Fengwei Wang, Lei Chen, ZTE Corporation; Jintao Zhang, MarineSat Network Technology Co., Ltd