A dynamic threshold-based smart contract abnormal invocation pattern detection method

The method for detecting abnormal smart contract call patterns by dynamically adjusting thresholds and analyzing multi-dimensional features solves the problems of high false positive and false negative rates and poor adaptability in existing technologies. It achieves accurate and real-time detection of abnormal smart contract calls, reduces operation and maintenance costs, and improves security protection capabilities.

CN122027355BActive Publication Date: 2026-07-10SHANGHAI CRIMINAL SCI TECH RES INST +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI CRIMINAL SCI TECH RES INST
Filing Date
2026-04-10
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing smart contract anomaly detection methods suffer from high false positive and false negative rates, poor adaptability, and high deployment costs, making it difficult to meet the real-time and accuracy requirements of blockchain applications.

Method used

A method for detecting abnormal smart contract call patterns based on dynamic thresholds is adopted. By monitoring and capturing multi-dimensional behavioral features in real time, dynamically adjusting the threshold, and combining multi-dimensional feature analysis, the method can achieve accurate and real-time detection of abnormal contract calls.

Benefits of technology

It effectively reduces false alarms and false negatives, adapts to fluctuations in contract behavior, reduces operational costs, meets the real-time detection needs of blockchain, and enhances security protection capabilities.

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Abstract

The application provides a kind of smart contract abnormal call mode detection method based on dynamic threshold, comprising: real-time monitoring and capturing the call request to target smart contract, extracting the multi-dimensional behavior characteristics of call request;For each multi-dimensional behavior characteristics, respectively maintain dynamic threshold, based on the corresponding historical data of each feature in the recent time window, periodically calculate and update dynamic threshold using a preset dynamic threshold algorithm;Determine whether each multi-dimensional behavior characteristics of call request exceeds the dynamic threshold range of corresponding time, if it exceeds, it is determined as abnormal call;When detecting abnormal call, trigger early warning mechanism and execute preset response measures;The application aims to dynamically adjust abnormal judgment standard, fuse multi-dimensional feature analysis, solve the problem of poor adaptability of fixed threshold, high cost of machine learning model, realize accurate and real-time detection of contract abnormal call, reduce security risk and operation and maintenance cost.
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