Blockchain Consensus With Node Trust Assessment and 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, and lack effective mechanisms for real-time threat detection and node trust evaluation.
Innovation Solution
A multi-layered blockchain framework, S4DP Ledger, utilizing a consensus protocol that incorporates a Multidimensional Trust Evaluation Model and a Multi-level Outlier-based Intrusion Detector to assess node maturity and honesty, combined with a Bloom Filter for trusted role assignment and a two-stage consensus process to ensure secure, efficient, and sustainable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional blockchain consensus mechanisms are used, then decentralization is achieved, but security against malicious nodes and real-time threat detection deteriorates
Solution Approach 1:
The consensus protocol is divided into two distinct stages: a first stage that assesses trust factors of nodes, and a second stage that executes block creation by selected trusted nodes. This segmentation allows the system to separately handle trust evaluation and consensus execution, improving security without overwhelming complexity
Solution Approach 2:
The system performs preliminary trust factor assessment in the first stage before proceeding to block creation in the second stage. By evaluating node trustworthiness (including maturity and honesty factors) in advance, the system ensures that only authenticated nodes participate in consensus, enhancing security proactively
2Productivity
If all nodes are allowed to participate in consensus, then decentralization is maximized, but energy consumption and processing overhead increase
Solution Approach 1:
Instead of requiring all nodes to participate in block creation, the system selects a subset of trusted nodes (those with validated trust factors) to execute the second stage of consensus. This partial participation approach maintains decentralization while significantly reducing energy consumption and processing overhead
Solution Approach 2:
The system dynamically changes the parameter of node participation based on trust factor assessment. Nodes transition from a default state to a trusted state (or vice versa) based on their evaluated maturity and honesty factors, optimizing consensus efficiency and energy usage adaptively
3Reliability
If real-time threat detection is implemented, then network security is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system implements continuous feedback mechanisms where nodes are monitored for anomalies and behavioral changes. The trust factor assessment incorporates real-time verification of node status, and nodes can be dynamically re-evaluated and reclassified based on their ongoing performance and behavior, creating a self-regulating security system
Solution Approach 2:
The trust evaluation model serves multiple functions simultaneously: it assesses node credibility for consensus participation, detects potential threats through anomaly monitoring, and provides a framework for real-time security verification. This multi-functionality reduces overall system complexity by consolidating security functions into a unified model
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.