Skunk — A Blockchain and Zero Trust Security Enabled Federated Learning Platform for 5G/6G Network Slicing
Eranga Bandara, Xueping Liang, Sachin Shetty, Ravi Mukkamala, Abdul Rahman, Wee Keong Ng
Abstract
The network slicing in 5G/6G mobile networks enables billions of connected devices to transmit data at higher rates than ever before. The high number of devices and the huge data rates result in configuration complexities and complex security management. Machine learning techniques could play a key role in managing these system complexities. While feder-ated learning (FL) has recently been proposed as an emerging paradigm to build privacy-preserving machine learning models, many of the existing systems involve centralized coordinators which are known to be vulnerable to attacks and privacy breaches. In addition, current FL models have weak support for transparency and provenance mechanisms. In this paper, we propose a Blockchain-based, Zero-trust Security-enabled Federated Learning system “Skunk” to address privacy and data provenance requirements. The proposed federated learning system also supports the requirements of 5G/6G networks. The sharding-based architecture in the blockchain enables the deployment of Skunk in 5G/6G network slice environments. As a use case of Skunk, we have considered a scenario with IoT device attacks in a 5G/6G network. The proposed FL models detect such attacks in the 5G/6G network sliced environment.