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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

–

–

–