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Table 2 Public blockchain network behavior risk awareness work comparison

From: Blockchain abnormal behavior awareness methods: a survey

Network behavior Behavior consequences Awareness difficulty Awareness methods Technical details Awareness accuracy Awareness speed Awareness availability Advantage Disadvantage Quantitative performance Data using
Eclipse attack Isolate the target node Hard Based on game theory Enhanced search technology based on dynamic voting mechanism (Ismail et al. 2015) Low Medium Using fully decentralized Need to reconfigure the routing table andrelated protocols Look up success rate: 90–99% Malicious detection rate: 55–60% (depends on parameters) Self-made data set by OMNeT++ simulator (Pongor 1993) and OverSim (Baumgart et al. 2007)
Based on supervised learning Use random forest classification algorithm to detect traffic packets (Xu et al. 2020) Medium Fast High Fast speed and high robustness Rely on the label accuracy of the attack data set Precision: 71% Recall: 95% F1: 0.81% Access connection packets in Ethereum network with attacks they made by script
Based on probabilistic model Suspicious block timestamp warning (Alangot et al. 2020) High Slow High Easy to deploy, no need to change network configuration and protocols Too slow Attack detection time: 3 h Malicious detection rate: 100% Need very long time Bitcoin block heads information in 2018–2019
Based on pattern matching Gossip protocol traffic analysis (Alangot et al. 2020) High Fast Medium Fast speed, no need to change network configur- ation and protocols Detection nodes are not easy to deploy Attack detection time: immediate Malicious detection rate: 97.58%
Selfish mining Malicious competition for block rewards Hard Based on game theory Block transaction additional expected confirmation height (Saad et al. 2019) High Fast Low Does not affect the growth and confirmation rate of the blockchain itself Loss of a certain transaction fee, need to change the block structure Success attack needs to reach 50% hash rate Bitcoin network
Based on probabilistic model Fork height simulation statistics (Chicarino et al. 2020) Analyze the fork probability caused by multiple attackers’ hash rates in a simulated environment Can only be tested in a small-scale simulation network Success attack needs to reach 30% hash rate Bitcoin network with simulation attacks by NS3 (Gervais et al. 2016)
51% Attack Double spend or malicious competition Easy Monitor large transa- ctions and the time of block production in the mining pool High Fast High Fast speed and high robustness