ETSI GR PDL 032 V1.1.1 (2025-04)

Permissioned Distributed Ledger (PDL); Artificial Intelligence for Permissioned Distributed Ledger

DGR/PDL-0032_AI_4PDL

General Information

• Status: Not Published
• Technical Committee: PDL - Permissioned Distributed Ledger
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 24-Apr-2025
• Completion Date: 22-Apr-2025

Standards Content (Sample)

GROUP REPORT
Permissioned Distributed Ledger (PDL);
Artificial Intelligence for Permissioned Distributed Ledger
Disclaimer
The present document has been produced and approved by the Permissioned Distributed Ledger (PDL) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR PDL 032 V1.1.1 (2025-04)

Reference
DGR/PDL-0032_AI_4PDL
Keywords
artificial intelligence, identity, PDL, scalability,
security
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3 ETSI GR PDL 032 V1.1.1 (2025-04)
Contents
Intellectual Property Rights . 8
Foreword . 8
Modal verbs terminology . 8
Executive summary . 8
Introduction . 9
1 Scope . 11
1.1 Description . 11
1.2 In scope . 11
1.3 Not in scope of the present document . 11
2 References . 12
2.1 Normative references . 12
2.2 Informative references . 12
3 Definition of terms, symbols and abbreviations . 19
3.1 Terms . 19
3.2 Symbols . 21
3.3 Abbreviations . 21
4 Enhancing PDL security using AI-based methods . 22
4.1 Introduction . 22
4.2 AI-Powered Anomaly Detection and Threat Identification in Real-Time . 23
4.2.1 Problem statement . 23
4.2.2 Using AI for Anomaly Detection and Real-Time Threat Detection . 23
4.2.3 Real-Time Monitoring and Analysis . 23
4.2.4 Pattern Recognition and Behavioural Analysis . 24
4.2.5 Adaptive Threat Detection . 24
4.2.6 Automated Response Mechanisms . 24
4.3 Enhanced Fraud Detection through Machine Learning Algorithms . 24
4.3.1 Problem statement . 24
4.3.2 Using AI to detect fraud . 25
4.3.3 Sophisticated Pattern Analysis . 25
4.3.4 Anomaly-Based Fraud Detection . 26
4.3.5 Predictive Fraud Analytics . 26
4.3.6 Continual Learning and Improvement . 26
4.3.7 Reduced False Positives . 27
5 Smart contract optimization using AI. 27
5.1 Introduction . 27
5.2 AI-Driven Smart Contract Code Analysis and Optimization . 27
5.2.1 Problem statement . 27
5.2.2 Using AI to handle such challenges . 27
5.2.3 Static Code Analysis . 28
5.2.4 Performance Optimization . 28
5.2.5 Security Enhancement . 29
5.2.6 Code Generation and Refactoring . 29
5.2.7 Natural Language Processing for Documentation . 29
5.3 Automated Testing and Verification of Smart Contracts . 30
5.3.1 Problem statement . 30
5.3.2 Tools for Improving reliability and reducing the risk of errors . 30
5.3.3 Automated Test Case Generation . 30
5.3.4 Fuzzing and Mutation Testing . 31
5.3.5 Formal Verification. 31
5.3.6 Symbolic Execution . 31
5.3.7 Continuous Integration and Deployment . 31
5.3.8 Learning from Past Vulnerabilities . 31
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6 AI-Enhanced Consensus Mechanisms in Permissioned Distributed Ledger Systems . 32
6.1 Consensus mechanisms for PDL functionality . 32
6.2 AI-enhanced consensus algorithms for faster and more efficient agreement . 32
6.3 Adaptive consensus mechanisms based on network conditions . 33
7 Data analytics and insights using AI . 33
7.1 Introduction and problem statement . 33
7.2 Analysing Large Volumes of Transaction Data for Valuable Insights using AI . 34
7.2.1 AI's capabilities to handle large volumes . 34
7.2.2 Pattern Recognition and Trend Analysis . 34
7.2.3 Anomaly Detection . 34
7.2.4 Customer Segmentation and Personalization . 35
7.2.5 Predictive Analytics . 35
7.2.6 Real-time Processing and Decision Making . 35
7.3 Predictive Analytics for Business Intelligence . 35
7.3.1 Predictive Analytics capabilities of AI . 35
7.3.2 Customer Behaviour Prediction . 35
7.3.3 Sales Forecasting . 36
7.3.4 Risk Assessment and Management . 36
7.3.5 Demand Forecasting . 36
7.3.6 Trend Analysis and Market Prediction . 36
7.3.7 Operational Efficiency Optimization . 36
7.3.8 Customer Lifetime Value Prediction . 37
8 Privacy-preserving techniques using AI . 37
8.1 Introduction and problem statement . 37
8.2 Developing Advanced Privacy-Preserving Computation Methods using AI . 38
8.3 Homomorphic Encryption and Secure Multi-Party Computation . 38
8.4 Federated Learning . 39
8.5 Differential Privacy in Machine Learning . 39
8.6 Generative Adversarial Networks (GANs) for Synthetic Data. 40
9 AI Tools for Network Optimization . 41
9.1 Problem statement . 41
9.2 Network Performance and Resource Allocation . 41
9.3 Predictive Maintenance of Network Nodes . 42
9.4 AI-Driven Network Topology Optimization . 43
9.5 Intelligent Data Sharding . 43
9.6 AI-Enhanced Network Security . 43
9.7 Energy-Efficient Network Operations . 44
9.8 AI-Powered Network Congestion Management. . 44
9.9 Adaptive Protocol Optimization . 44
10 Governance and compliance using AI . 45
10.1 Introduction and problem statement . 45
10.2 AI Assisted Governance Rules and Compliance Checks Enforcement . 45
10.3 AI Assisted Automated Auditing and Reporting . 46
10.4 AI-Enhanced Governance Participation . 47
10.5 Regulatory Compliance Monitoring . 47
10.6 Intelligent Dispute Resolution . 48
11 Identity management using AI . 48
11.1 Introduction and problem statement . 48
11.2 AI-Enhanced Identity Verification and Management Processes . 49
11.2.1 AI-Powered Facial Recognition . 49
11.2.2 AI-Powered Document Verification System . 49
11.2.3 Anomaly Detection: AI-Powered Behavioural Biometrics for Continuous Authentication . 49
11.3 Additional Scenarios and Examples . 49
11.3.1 Federated Identity Management . 49
11.3.2 Adaptive Access Control . 50
11.3.3 Identity Recovery and Remediation . 50
11.3.4 Decentralized Identity Verification . 50
11.3.5 Cross-Chain Identity Management . 50
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12 AI-Assisted PDL Interoperability . 51
12.1 PDL Interoperability in the context of AI - problem statement . 51
12.2 AI-Facilitated Cross-Chain Communication and Data Exchange . 51
12.3 Smart Routing of Transactions Between Different Ledgers . 52
13 AI based PDL Scalability solutions . 52
13.1 Problem statement . 52
13.2 Developing More Efficient Scaling Solutions using AI . 52
13.3 Dynamic Sharding Based on Network Traffic and Usage Patterns . 53
14 Conclusion and Recommendations . 53
Annex A: List of AI-tools referenced in the present document with brief descriptions and
application for PDL . 55
A.1 Examples related to clause 4 (Enhanced security) . 55
A.1.1 Examples of AI Algorithms for Continuous Monitoring . 55
A.1.1.1 Temporal Graph Convolutional Networks (TGCNs) . 55
A.1.1.2 Federated Attention Mechanism with Differential Privacy . 55
A.1.1.3 Hierarchical Long Short-Term Memory Networks with Adaptive Thresholding . 56
A.1.2 Examples of Advanced Machine Learning Models for Pattern Recognition . 57
A.1.2.1 Graph Neural Networks (GNNs) . 57
A.1.2.2 Transformer-based Models . 57
A.1.2.3 Deep Clustering Networks (DCNs) . 58
A.1.3 Examples of Adaptive AI Systems for Evolving Threat Detection . 58
A.1.3.1 Continual Learning Networks . 58
A.1.3.2 Meta-Learning Systems . 59
A.1.3.3 Reinforcement Learning for Adaptive Security . 59
A.1.4 Examples of AI Systems for Automated Response Mechanisms in PDL Networks . 60
A.1.4.1 Reinforcement Learning-based Autonomous Defence Systems . 60
A.1.4.2 Federated Learning-based Collaborative Defence Systems . 60
A.1.4.3 Explainable AI (XAI) for Automated Incident Response . 61
A.1.5 Examples of AI-Based Machine Learning Models for Fraud Detection . 61
A.1.5.1 Graph Neural Networks (GNNs) for Fraud Detection . 61
A.1.5.2 Transformer-based Models for Sequential Fraud Detection . 61
A.1.5.3 Federated Deep Learning for Privacy-Preserving Fraud Detection . 62
A.1.6 Examples of unsupervised learning algorithms used to establish baseline behaviours . 62
A.1.6.1 Graph Autoencoders (GAEs) for Network Behaviour Modelling . 62
A.1.6.2 Variational Autoencoders (VAEs) for Anomaly Detection . 63
A.1.6.3 Temporal Convolutional Networks (TCNs) for Time Series Analysis . 63
A.1.7 Examples of Predictive Machine Learning Models for Fraud Detection . 64
A.1.7.1 Graph Neural Networks (GNNs) with Temporal Attention . 64
A.1.7.2 Transformer-based Models with Self-Supervised Pre-training . 64
A.1.7.3 Federated Deep Learning with Differential Privacy . 65
A.1.8 Examples of Continuous Learning Machine Learning Models for Fraud Detection . 65
A.1.8.1 Online Adaptive Graph Neural Networks (OAGNNs) . 65
A.1.8.2 Incremental Learning with Ensemble Methods . 66
A.1.8.3 Federated Continual Learning. 66
A.1.9 Examples of Machine Learning Models for Reducing False Positives in Fraud Detection . 67
A.1.9.1 Attention-based Graph Neural Networks with Explainable AI . 67
A.1.9.2 Hybrid Models Combining Anomaly Detection with Supervised Learning . 67
A.1.9.3 Federated Learning with Adaptive Boosting . 68
A.2 Examples related to clause 5 (Smart contract optimization using AI) . 68
A.2.1 Examples of AI-Powered Static Code Analysis Tools . 68
A.2.1.1 DeepCode . 68 ®
A.2.1.2 Infer . 69 ®
A.2.1.3 CodeQL . 69
A.2.2 Examples of AI-Based Machine Learning Algorithms for Smart Contract Optimization . 69
A.2.2.1 Deep Reinforcement Learning for Dynamic Gas Optimization . 69
A.2.2.2 Graph Neural Networks with Attention for Code Pattern Recognition . 69
A.2.2.3 Transformer-based Model with Transfer Learning for Cross-Language Optimization . 70 ®
A.2.2.4 Hyperledger Caliper . 70
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A.2.2.5 OptSmart . 70
A.2.3 Examples of AI Algorithms for Identifying Smart Contract Vulnerabilities . 71
A.2.3.1 Graph Neural Networks (GNNs) with Semantic-Aware Embedding . 71
A.2.3.2 Transformer-based Models with Transfer Learning . 71
A.2.3.3 Reinforcement Learning with Symbolic Execution . 71
A.2.4 Examples of AI Algorithms for Code Generation and Optimization in PDL Platforms . 72
A.2.4.1 Large Language Models with Few-Shot Learning . 72
A.2.4.2 Graph-to-Code Neural Networks with Attention . 72
A.2.4.3 Hierarchical Transformers with Code Semantic Embedding . 72
A.2.5 Examples of AI-Powered NLP Tools for Smart Contract Documentation . 73
A.2.5.1 CodeBERT-based Documentation Generation . 73
A.2.5.2 Graph-to-Sequence Neural Networks for Contract Summarization. 73
A.2.5.3 Hierarchical Transformer with Code-Text Alignment . 73
A.2.6 Examples of AI-Based Machine Learning Algorithms for Smart Contract Test Case Generation . 74
A.2.6.1 Deep Reinforcement Learning for Adaptive Fuzzing . 74
A.2.6.2 Graph Neural Networks with Symbolic Execution . 74
A.2.6.3 Transformer-based Models with Program Synthesis . 74
A.2.7 Examples of AI-Driven Fuzzing Techniques for Smart Contract Testing. 75
A.2.7.1 Reinforcement Learning-based Adaptive Fuzzing . 75
A.2.7.2 Neuro-Symbolic Execution with Mutation . 75
A.2.7.3 Evolutionary Fuzzing with Natural Language Processing (NLP) . 76
A.2.8 Examples of AI-Based Tools for Formal Verification of Smart Contracts . 76
A.2.8.1 Neural-Guided Theorem Prover (NGTP) . 76
A.2.8.2 Transformer-based Model Checker (TMC) . 76
A.2.8.3 Graph Neural Network-based Invariant Synthesizer (GNNIS) . 77
A.2.9 Examples of AI-Enhanced Symbolic Execution Techniques for Smart Contract Analysis . 77
A.2.9.1 Neural-Guided Symbolic Execution (NGSE) . 77
A.2.9.2 Reinforcement Learning-based Concolic Testing (RLCT) . 78
A.2.9.3 Graph Neural Network-Enhanced Symbolic Execution (GNN-SE) . 78
A.2.10 Examples of AI-Based Tools for Smart Contract DevSecOps Pipelines . 78
A.2.10.1 SmartBugs: AI-Enhanced Vulnerability Detection Pipeline . 78
A.2.10.2 ContractGuard: Automated Verification and Deployment Framework . 79
A.2.10.3 AISecOps: AI-Driven Security Operations for Smart Contracts . 79
A.2.11 Examples of AI Systems for Continuous Improvement in Smart Contract Security . 80
A.2.11.1 VELMA: Vulnerability-driven Evolutionary Learning for Smart Contract Auditing . 80
A.2.11.2 SCSCAN: Self-Correcting Smart Contract Vulnerability Scanner . 80
A.2.11.3 ASTRAEA: Adaptive Smart conTRact Auto-Evaluation and Auditing . 80
A.3 Examples related to clause 8: Privacy-preserving techniques . 81
A.3.1 Examples of Federated Learning . 81
A.3.1.1 PySyft . 81
A.3.1.2 Flower . 81
A.3.1.3 OpenFL . 81
A.3.1.4 FedML . 81
A.3.2 Examples of Differential Privacy in Machine Learning . 81
A.3.2.1 Differentially Private Stochastic Gradient Descent (DP-SGD) . 81
A.3.2.2 Differentially Private Follow The Regularized Leader (DP-FTRL) . 82
A.3.2.3 Gaussian Differential Privacy (GDP) . 82
A.3.3 Examples of Generative Adversarial Networks (GANs) for synthetic data generation . 82
A.3.3.1 Privacy-Preserving Synthetic Data Generation Using Conditional GANs . 82
A.3.3.2 TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks . 83
A.3.3.3 SynSig: Generating Synthetic Signatures for Large-Scale Time Series Anomaly Detection . 84
A.4 Examples related to clause 11 (Identity management using AI) . 85
A.4.1 AI-Powered Facial Recognition . 85
A.4.1.1 Description . 85
A.4.1.2 Use Case . 85
A.4.2 AI-Powered Document Verification System . 85
A.4.2.1 Description . 85
A.4.2.2 Key components . 85
A.4.2.3 Process . 86
A.4.2.4 Performance . 86
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A.4.3 Anomaly Detection: AI-Powered Behavioural Biometrics for Continuous Authentication . 86
A.4.3.1 Description . 86
A.4.3.2 Examples . 86
A.4.3.3 Application . 87
A.4.3.4 Key Advantages . 87
A.5 Examples and recent research related to clause 12 (AI-Assisted PDL Interoperability). 87
A.5.1 AI-Facilitated Cross-Chain Communication and Data Exchange . 87
A.5.1.1 Examples of AI applications in cross-chain communication . 87
A.5.1.2 Recent research in this area. 87
A.5.2 Examples of AI applications in Smart Routing of Transactions Between Different Ledgers . 88
A.5.2.1 Reinforcement Learning for Optimal Path Finding . 88
A.5.2.2 Predictive Analytics for Network Congestion . 88
A.5.2.3 Federated Learning for Collaborative Routing Optimization . 88
A.5.2.4 Graph Neural Networks for Dynamic Topology Analysis. 88
A.5.2.5 Multi-Agent Systems for Decentralized Routing . 88
A.5.3 Additional Scenarios and Examples . 88
A.6 Examples and recent research related to clause 13 (AI based PDL Scalability solutions) . 89
A.6.1 Developing More Efficient Scaling Solutions using AI . 89
A.6.1.1 Adaptive Consensus Optimization . 89
A.6.1.2 Intelligent Sharding. 89
A.6.1.3 Smart Contract Parallelization . 89
A.6.1.4 Predictive Caching . 89
A.6.1.5 Network Topology Optimization . 89
A.6.2 Dynamic Sharding Based on Network Traffic and Usage Patterns . 90
A.6.2.1 Predictive Sharding . 90
A.6.2.2 Adaptive Shard Allocation . 90
A.6.2.3 Intelligent Cross-Shard Transaction Management . 90
A.6.2.4 Anomaly-Aware Sharding . 90
A.6.2.5 Federated Learning for Collaborative Sharding . 90
A.6.3 Additional Scenarios and Examples . 90
History . 92

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8 ETSI GR PDL 032 V1.1.1 (2025-04)
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Permissioned Distributed
Ledger (PDL).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are
...