Trusted-Node Consensus for Low-Latency AI Threat Detection
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Solution Overview
Problem
Existing blockchain systems face challenges in achieving a balance between decentralization, security, scalability, and sustainability, particularly in the context of smart systems, where trust and privacy are critical, and they struggle with high latency and transaction processing throughput.
Innovation Solution
The S4DP Ledger employs a consensus protocol that utilizes a Multidimensional Trust Evaluation Model and a Bloom Filter-based algorithm to select trusted nodes, combined with a two-stage consensus process and optimized communication mechanisms to ensure secure, scalable, and sustainable blockchain operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional blockchain consensus mechanisms are used, then security and decentralization are maintained, but transaction processing throughput is limited and latency is high
Solution Approach 1:
The system segments the network into trusted validator nodes and regular nodes. Validator nodes are selected based on trust scores and are responsible for consensus operations, while regular nodes perform transactions. This segmentation allows faster processing without compromising security, as the trusted subset handles critical consensus functions.
Solution Approach 2:
The system dynamically changes the parameter of node selection by using trust scores calculated from behavioral analysis and historical performance. Nodes with higher trust scores are preferentially selected as validators, allowing the system to adaptively optimize throughput while maintaining security through data-driven parameter adjustment.
2Reliability
If more nodes are added to the network to enhance decentralization, then security and trust are improved, but system complexity and communication overhead increase
Solution Approach 1:
The system introduces trust scores and behavioral analysis mechanisms as intermediaries between nodes. These intermediaries automatically evaluate node reliability and facilitate coordinated interactions, reducing the complexity of managing decentralized node relationships while maintaining security through automated trust verification.
3Reliability
If real-time threat detection is implemented across all nodes, then security is enhanced, but energy consumption and processing overhead increase
Solution Approach 1:
The system segments threat detection responsibilities by implementing anomaly detection primarily at validator nodes, which have higher trust scores and are more reliable. Regular nodes perform basic security functions. This segmentation reduces overall energy consumption while maintaining security, as the more resource-intensive detection is concentrated on trusted nodes.
4Reliability
If strict authentication requirements are enforced for all devices, then security and trustworthiness are improved, but network scalability and ease of operation deteriorate
Solution Approach 1:
The system performs preliminary trust evaluation and authentication during the node joining process. Trust scores are calculated in advance based on device characteristics, historical behavior, and verification data. This preliminary action ensures security requirements are met before full network access is granted, simplifying ongoing operations while maintaining trustworthiness.
Data Source
AI summary
Systems and methods are provided utilizing a consensus protocol that assesses a trust factor of one or more nodes, and which executes a block creation process by selecting and assigning trusted nodes, based on the trust factor assessment.