Blockchain Node Verification Feedback for Malicious Node Detection
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Solution Overview
Problem
Malicious consensus nodes in a blockchain network can generate abnormal blocks that may pass verification, compromising network security if continuously submitted, as existing systems lack effective methods to detect and mitigate such nodes.
Innovation Solution
A method to detect abnormal consensus nodes by obtaining and counting verification results from multiple nodes, determining a target verification result, updating historical quantities of abnormal verifications, and identifying nodes with excessive abnormal verifications for blacklisting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple consensus nodes continuously submit abnormal blocks for verification, then the malicious node can potentially compromise network security, but the existing verification mechanism cannot effectively detect and eliminate such nodes
Solution Approach 1:
The patent implements a feedback mechanism where each consensus node's verification results are collected and analyzed. The target consensus node compares its own verification result with those of other nodes, and when discrepancies are detected, it updates the abnormal verification quantity for the corresponding node. This continuous feedback loop enables the system to dynamically identify and respond to malicious behavior, resolving the contradiction between maintaining network security and detecting abnormal nodes.
Solution Approach 2:
The patent introduces an intermediary detection mechanism where a target consensus node acts as a mediator to compare verification results from multiple nodes. Instead of direct peer-to-peer confrontation, the target node mediates the comparison process, aggregates verification quantities, and determines abnormal nodes based on threshold comparisons. This intermediary approach enables effective detection while maintaining the decentralized nature of the blockchain network.
2Reliability
If the blockchain network implements strict verification for all blocks, then network security improves, but the continuous submission of abnormal blocks by malicious nodes can still eventually compromise security
Solution Approach 1:
The patent implements preliminary action by proactively detecting and identifying abnormal consensus nodes before they can successfully compromise the network. The system continuously monitors verification results, compares them across nodes, and maintains a record of abnormal verification quantities. When a node's abnormal verification quantity exceeds the total verification quantity, the system can preemptively blacklist the node, preventing potential security breaches rather than reacting after damage occurs.
Solution Approach 2:
The feedback mechanism tracks the cumulative verification results of each consensus node over time. By continuously updating the abnormal verification quantity and comparing it against the total verification quantity, the system creates a dynamic assessment of node reliability. This long-term feedback approach enables the network to identify persistently malicious nodes and take corrective action, thereby limiting the duration of malicious node influence on the network.
3Reliability
If the system blacklists abnormal consensus nodes to prevent abnormal block uploads, then network integrity is maintained, but the detection and blacklisting process requires additional computational resources
Solution Approach 1:
The patent implements self-service by enabling each consensus node to participate in the detection process using its own verification capabilities. The target consensus node leverages its existing verification function to compare results with other nodes, without requiring separate specialized detection hardware or complex external verification systems. The blacklisting decision is made autonomously by the target node based on its own analysis of verification discrepancies, reducing the need for centralized control and minimizing additional system complexity.
Data Source
AI summary
This application discloses a blockchain-based data detection method performed by a computer device. The method includes obtaining respective block verification results of at least two consensus nodes for a to-be-verified block; counting valid verification results and invalid verification results among the respective block verification results of the at least two consensus nodes; determining a target verification result for the block according to the counts, comparing the target verification result with the respective block verification results of the at least two consensus nodes for the block, and updating a historical quantity of abnormal verifications corresponding to each consensus node according to a result of the comparison, to obtain a target quantity of abnormal verifications of each consensus node counted by the target consensus node; and determining an abnormal consensus node among the at least two consensus nodes according to the target quantity of abnormal verifications counted by the target consensus node.