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Table 6 Consortium blockchain abnormal behavior risk awareness work comparison

From: Blockchain abnormal behavior awareness methods: a survey

Awareness purpose Technical methods Technical details Applicable scene Advantage Disadvantage Quantitative performance Data using
Identity tracing Group signature and ring signature The identity of the signer is confidential but verifiable, and the administrator can open the identity of the signer (Zheng et al. 2018; Fujisaki and Suzuki 2007; Liu et al. 2004) Consortium blockchain acquiring anonymity while can tracing identity in the abnormal situation Satisfy identity anonymity and linkability Long preparation time and slow speed before signing Operations of Multiplication: 20 Operations of Exponentiation: 27 Low computational complexity
Public key and certificate binding Register the public key with a trusted third party to in- crease the credibility of the address (Ateniese et al. 2014) Consortium blockchain with single institution of the supervision center Fast and accurate identity tracing Abuse of supervisory power Script efficiency is as good as Bitcoin
Cooperative registration of public keys based on multi-party secure computing (El Defrawy and Lampkins 2014) Consortium blockchain with multi-institutions of supervision centers Can prevent abuse of supervisory power Low tracking efficiency and slower speed
With the change of the user’s public key, only a single registration to the supervision center (Li et al. 2021) Consortium blockchain with single institution of the supervision center Reduce the burden on users and the supervision center Abuse of supervisory power Efficiency is as good as Groth–Sahai proof system (Groth and Sahai 2008)
Biometrics blockchain (BBC) Using biometric information to track malicious accounts on-chain by BBC architecture (Alharthi et al. 2021) Internet of vehicles with consortium blockchain Use biometric information to label message senders for privacy and ensure message credibility Latency time of chain updated Packet Loss Rate: Less than 5% Computational Cost: 0.1–0.3 ms Self-made chain with simulation attacks by OMNeT++ (Pongor 1993)
Attack on PBFT leaders Based on reputation schemes Use reputation model to evaluate leaders’ scores and perceive leader nodes (Lei et al. 2018) Consortium blockchain using PBFT consensus mechanism Identify malicious leader leader nodes in time The effects in non-experimental environments need to be further tested Feasibility: Delay time with in 22.0 s Reliability: Linear Self-made chain prototype
Forensic support based Use cryptographic primitives such as aggregate signatures and commitments to take BFT forensic support (Sheng et al. 2021) Consortium blockchain using BFT consensus mechanism Forensic support can visualize Large scale test need to be further tested
Auditing under privacy protection Zero-knowledge proof and commitment Additive homomorphism commitment to hide sensitive data and complete the audit with zero-knowledge proof guaranteeing audit reliability (Narula et al. 2018) Consortium blockchain with audit content kept confidential High privacy and audit reliability Limited auditing operations Computational Cost: Linear (Validate for 20 nodes in less than 200 ms) Auditing Time with More Nodes: Linear Self-made chain prototype
Collusion attack Based on reputation schemes Use Bayesian inference model to evaluate reputation scores and perceive collusion attacks (Yang et al. 2018) Internet of vehicles with consortium blockchain High feasibility in consortium ranges Latency time of chain updated Feasibility: Less than 1 s Reliability: Exponent Users in vehicular and blockchain simulation platform
Use the reputation chain to improve the performance of the transaction chain and perceive collusion attacks (Huang et al. 2020) E-commerce environment with consortium blockchain Sharding improves chain’s throughput The effects in non-experimental environments need to be further tested Feasibility: 20.4 s delay time at least Reliability: Linear Self-made chain prototype with simulated users
Use smart contracts to evaluate reputation scores and resist collusion attacks (Zhou et al. 2021) E-commerce environment with consortium blockchain High feasibility and reliability The robust effect on other attacks remains to be verified Feasibility: Less than 0.8 s Reliability: Constant Partial Ethereum users participant in
“Govern blockchains by blockchains” Double-chain architecture The double-chain consists of a detection chain and a data public chain. The detection chain deploys multi-feature models to detect malicious behaviors on the data public chain (Gu et al. 2018) Consortium blockchain with multi-institutions of supervision centers High accuracy and small scale high detection speed Large scale testing is inefficient Accuracy: 92.5% Recall: 94.6% F1: 93.5% Drebin Dataset (Arp et al. 2014)
The double-chain consists of the transaction chain and the custody chain. The custody chain uses a neural network to identify illegal transactions, and the double-chain anchors the public blockchain (Wu et al. 2020a) Consortium blockchain with multi-institutions of supervision centers Fast transaction speed, high scalability, and strong credibility Practicability needs to be verified Accuracy: 90.1% Recall: 18.5% F1: 30.8% Elliptic Dataset