Blockchain Anomaly Detection via Cross-Merklization
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Solution Overview
Problem
Current computer-based transactional record keeping systems face challenges in detecting and remediating intrusions, especially in larger entities with high transaction volumes, as existing intrusion detection and prevention methods often fail to detect anomalies in a timely manner, leading to potential data exposure and significant costs.
Innovation Solution
A blockchain network is implemented to detect anomalies by cross-merklizing data across participating blockchains, allowing for self-detection or detection by peers without sharing sensitive information, using cryptographic hashes and proof-of-work to ensure data integrity and immutability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection methods are used in high-volume transaction systems, then system complexity is reduced, but detection speed and reliability deteriorate
Solution Approach 1:
The system segments the detection function into distributed blockchain nodes, where each node independently validates transactions and maintains a copy of the ledger. This segmentation allows parallel processing of transactions across multiple nodes, improving detection reliability without centralizing complexity. Each node performs local validation while contributing to global security through cryptographic consensus.
Solution Approach 2:
The blockchain protocol acts as an intermediary layer between transaction sources and the ledger, providing automated anomaly detection through cryptographic validation. This intermediary mechanism detects inconsistencies without requiring complex external monitoring systems, as the protocol itself enforces integrity rules and rejects invalid transactions automatically.
2Measurement precision
If comprehensive data monitoring is implemented to detect intrusions, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary cryptographic validation of transactions before they are added to the blockchain. Each transaction is signed with digital signatures and validated against consensus rules in advance, ensuring detection accuracy is built into the transaction structure itself rather than requiring post-hoc analysis of complete datasets.
Solution Approach 2:
The system replaces mechanical data scanning and analysis with cryptographic verification mechanisms. Instead of processing and analyzing transaction data contents, nodes verify cryptographic signatures and consensus validity, achieving high detection accuracy through mathematical proofs rather than computational inspection of data patterns.
3Speed
If real-time anomaly detection is implemented, then intrusion response time improves, but system overhead increases
Solution Approach 1:
The blockchain network performs self-validation through distributed consensus mechanisms. Each node independently verifies transactions and detects anomalies without requiring external monitoring infrastructure. This self-service approach enables real-time detection across the network while distributing computational overhead across all participating nodes rather than concentrating it in a single detection system.
Data Source
AI summary
Systems and methods of detecting anomalies in a computing system are disclosed. The computing system can be a member of a blockchain network of a plurality of blockchains. Digests of blocks may be passed between blockchains of the plurality of blockchains, which enables each member of the blockchain network to verify an immutable record of data transactions, free of the mutual trust requirement of a typical blockchain environment. The passing of blockchain block digests further enables a member of the blockchain network to assist another member of the blockchain network to identify an anomaly within moments of when the anomaly first occurs.