Deep-Learning-Driven Secure Offline Blockchain in a Cyber-Physical Social System

A cyber-physical social system (CPSS), which integrates the social world with the cyber world and physical world, plays a crucial role in a more intelligent and efficient network. Recent advances in blockchain have significant impacts on securing the transactions in CPSS. Nevertheless, the deployment of blockchain faces fundamental engineering challenges. Specifically, in remote or disaster areas, users of CPSS typically cannot effectively connect to the blockchain system in real time due to the deficiency of Internet connection. In busy urban areas, however, users are reluctant to frequently connect to the blockchain system due to the costly communication connection. Thus, the offline blockchain system, which enables direct secured offline transaction channels among users in CPSS and efficiently synchronizes batch transactions to the global blockchain system, is a promising solution to address these problems. This article presents a novel CPSS architecture that can realize secure transactions for users participating in offline blockchain transactions by making use of an AI-based algorithm. We first introduce three typical offline blockchain transaction environments in CPSS, followed by analysis of the security issues of the proposed architecture. In addition, we present the hashed time locked contract (HTLC) established for the CPSS users participating in an offline blockchain transaction. To detect potential malicious attackers in offline transactions, we introduce an LSTM-based recurrent neural network (RNN) model to detect the malicious transaction witness. Numerical results are presented to validate the effectiveness of this model.