Explore Patterns to Detect Sybil Attack during Federated Learning in Mobile Digital Twin Network

Wenmiao Zhang, Chong Yu, Zhenyu Meng, Shuaiqi Shen, Kuan Zhang

Abstract: Digital twins represent users in the cyber world and interact between users and network controllers to better manage the mobile network. Due to communications and other resource constraints, transmitting raw data for a traditional, centralized machine learning in the mobile network has been replaced by federated learning. Federated learning allows participants to train a complex model in a distributed manner, through a group of participants' local training and a global aggregation with model updates as feedback. Although federated learning can save communications costs, address data heterogeneity and protect privacy by stopping the raw data transmission, it faces various se-curity challenges. For example, poisoning attacks may inject false models or modify existing model parameters to bias the gradient descent of federated learning. Some literature attempted to detect poisoning attacks, but the attackers can still strengthen their power by creating many identities to build their group advantage, which overturns the existing detection. In this paper, we propose a digital-twin-based Sybil detection by creating new community detection among participants in federated learning. Specifically, we first identify Sybil attackers on several levels according to their attacking strength and strategies. Then, we integrate digital twins as a side channel to distinguish Sybil identities which in fact belong to the same attacker. This could leak the attacker's correlated behavior patterns which are automatically recorded in digital twins. Under this observation, we build a DT-graph that tightly connects Sybil-controlled identities belonging to the same attacker. We propose a graph-based community detection algorithm to further partition the DT-graph and distinguish Sybil attacks. Extensive simulations validate our proposed method compared with existing work.