Abstract
Smart contracts allow verifiable operations to be executed in blockchains, bringing new possibilities for trust establishment in trustless scenarios. However, smart contracts are cumbersome when used as security mechanisms in security scenarios due to two reasons: they have limited power and are inert to changes.In order to mitigate the two problems of employed smart contracts, we propose LSC, a framework for online auto-update smart contracts in blockchain-based log systems, to enable self-adaptive log anomaly detection via smart contracts. Time-varying log anomaly detection patterns are extracted by self-adaptive machine learning log anomaly analysis and are continuously fed to the contracts. The framework allows smart contracts to be automatically updated to express the patterns in low-cost ways. The anomaly detection strategies for audit log systems are shared and collaboratively enforced amongst network nodes to defend against targeted detection evasion. We provide a plain prototype as a proof of the feasibility and efficiency of LSC in log systems.
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