Smart Micro-Grid Systems Security and Privacy (eBook)

Preface 7
Acknowledgements 9
Contents 11
Contributors 12
List of Reviewers 13
1 Power Systems: A Matter of Security and Privacy 14
1.1 Context and Motivation 15
1.2 Smart Grids and Smart Micro-Grids 15
1.3 State Estimation and Authentication in Smart Grids 16
1.3.1 Attacks on Power State Estimation 16
1.3.2 Authentication in Smart Grids 17
1.3.3 Attacks on Authentication in Smart Grids 17
1.4 Resource Constrained Smart Micro-grids 18
1.4.1 Architectures Matter 18
1.4.2 Power Auctioning and Cheating 19
1.4.3 Inferring Private Behaviours 19
1.5 Discussions 20
References 20
2 A Review on Attacks and Their Countermeasures in Power System State Estimation 22
2.1 Introduction 22
2.2 Power System State Estimation 23
2.2.1 Observability 24
2.2.2 State Estimation 25
2.2.3 Bad Data Analysis 26
2.3 Descriptive Comparison Between Various Strategies 27
2.3.1 Origin of False Data Injection Attacks 28
2.3.1.1 Description 28
2.3.1.2 Discussion 29
2.3.2 Immunity by Protecting Critical Measurements 29
2.3.2.1 Description 29
2.3.2.2 Discussion 30
2.3.3 Minimum Cost Stealth Attacks 30
2.3.3.1 Description 30
2.3.3.2 Discussion 31
2.3.4 Sparse Attacks Corrupting Two Injection Meters 31
2.3.4.1 Description 32
2.3.4.2 Discussion 32
2.3.5 Stealth Attacks Involving Exactly One Control Centre 32
2.3.5.1 Description 33
2.3.5.2 Discussion 33
2.3.6 Re-ordering or Swapping Attacks 34
2.3.6.1 Description 34
2.3.6.2 Discussion 34
2.3.7 Random and Structured Delay Attacks 34
2.3.7.1 Description 35
2.3.7.2 Discussion 35
2.3.8 Subspace Methods for Data Attacks 35
2.3.8.1 Description 36
2.3.8.2 Discussion 36
2.3.9 Detectable Jamming Attacks 37
2.3.9.1 Description 37
2.3.9.2 Discussion 37
2.3.10 Data Injection Attacks with Multiple Adversaries 38
2.3.10.1 Description 38
2.3.10.2 Discussion 38
2.4 Conclusion 39
References 40
3 An Anonymous Authentication Protocol for the Smart Grid 42
3.1 Introduction 43
3.1.1 Background 43
3.1.2 Contribution 44
3.1.3 Related Work 45
3.1.3.1 Privacy Preservation on a Smart Grid 45
3.1.3.2 Group Signatures 46
3.2 Requirements 46
3.2.1 Participants and Their Business Logic 46
3.2.2 Security Requirements 48
3.2.2.1 Assumption 48
3.2.2.2 Unlinkability 48
3.2.2.3 Undeniability 48
3.2.2.4 Unforgeability for Charging Information 48
3.3 Proposed Anonymous Authentication Protocol 49
3.3.1 Building Blocks 49
3.3.1.1 Controllable-Linking Group Signatures (CL-GS) 49
3.3.1.2 Digital Signatures 50
3.3.2 Ground Rules of the Proposed Protocol 50
3.3.2.1 Ground Rules for the Proposed Protocol 50
3.3.2.2 Initial Setting 51
3.3.3 Construction 51
3.3.3.1 Preparation Phase 52
3.3.3.2 Sign Phase 52
3.3.3.3 Verification Phase 53
3.3.4 Security Analysis of the Proposed Protocol 53
3.4 Instantiation: Token-Dependent Controllable-Linkability Group Signatures 54
3.4.1 The Syntax of TDCL-GS 54
3.4.2 Security Definition 55
3.4.3 Construction 55
3.4.4 Security Analysis 56
3.4.4.1 Assumptions 57
3.4.4.2 Correctness 57
3.4.4.3 Anonymity 58
3.4.4.4 Unforgeability 59
3.4.4.5 Linking Soundness 59
3.4.4.6 Traceability 59
3.4.5 Application to the Proposed Protocol 59
3.5 Discussion 60
3.5.1 The Potential Architecture of the Proposed Protocol 60
3.5.2 Experiment 62
3.5.2.1 Experimental Result 62
3.6 Conclusion 64
References 64
4 Attacks on Authentication and Authorization Modelsin Smart Grid 66
4.1 Introduction 66
4.1.1 Electric Grid to Smart Grid 68
4.1.2 Smart Grid to Smart Micro Grid 69
4.2 Related Works 71
4.2.1 Traditional Electric Grid System 72
4.2.2 Smart Grid and Micro Smart Grid Systems 72
4.3 Security Challenges in Smart Micro Grid 74
4.3.1 Security Attacks in Smart Micro-Grid Systems 78
4.4 Conclusion 81
References 82
5 A Resilient Smart Micro-Grid Architecture for Resource Constrained Environments 84
5.1 Introduction 85
5.1.1 Context and Motivation 85
5.1.2 Problem Statement 86
5.1.3 Contributions 86
5.1.4 Outline 87
5.2 State-of-the-Art on Power Network Security 88
5.2.1 Smart Grid Architectures 88
5.2.2 Smart Micro-Grid Architectures 89
5.3 Resource Constrained Smart Micro-Grid: Network Model 90
5.3.1 Notation 91
5.3.2 Power Network 92
5.3.3 Communication Network 94
5.3.4 Control Network 96
5.4 Micro-Grid Operation 96
5.4.1 The Feedback Control Loop 97
5.4.2 Metering 97
5.4.3 Power Demand Management 99
5.4.4 Power Demand Forecasting 101
5.4.5 Power Demand Scheduling 103
5.5 Adversarial Models 105
5.5.1 False Data Injection Attacks 105
5.5.1.1 Scenario 1 106
5.5.1.2 Scenario 2 106
5.5.2 Denial-of-Service Attacks 107
5.5.2.1 Scenario 1 107
5.5.2.2 Scenario 2 108
5.5.3 Coalition and Collusion Attacks 108
5.5.3.1 Scenario 1 109
5.5.3.2 Scenario 2 110
5.6 Conclusions and Future Work 111
5.6.1 Summary 111
5.6.2 Future Work 111
References 112
6 The Design and Classification of Cheating Attacks on Power Marketing Schemes in Resource Constrained Smart Micro-Grids 115
6.1 Introduction 116
6.2 Framework Design 119
6.2.1 Attack Domain Model 120
6.2.1.1 Decentralised CDA Algorithm 121
6.2.2 Attacker Behaviour 124
6.2.2.1 Attacker Intent 125
6.2.2.2 Attacker Capability Model 126
6.2.2.3 Attacker Rational 126
6.2.3 Attacker Model 127
6.2.4 Attack Model 127
6.2.5 Attacks 128
6.3 Cheating Attack Cases 129
6.3.1 Victim Strategy Downgrade 130
6.3.2 Dynamic Collusion Attack 133
6.3.3 Evasive Agent Attack 135
6.3.4 Adaptive Aggressive Strategy Manipulation 138
6.3.4.1 A Market-Information Attack 139
6.3.4.2 Adaptive-Parameters Attack 140
6.4 Classifying Cheating Attacks 142
6.5 Formulating Counter Measures 144
6.5.1 Exception Handling Approach 144
6.5.1.1 Local Market Procedure Extension 144
6.5.1.2 TA-Sentinel Execution 146
6.5.1.3 T–Sentinel Execution 147
6.5.2 Cheating Detection and Mitigation 148
6.5.2.1 Performance Analysis 150
6.6 Related Work 150
6.7 Conclusions 152
References 152
7 Inferring Private User Behaviour Based on Information Leakage 157
7.1 Introduction 158
7.1.1 Context and Motivation 158
7.1.2 Problem Statement 159
7.1.3 Contributions 159
Outline of the Chapter 159
7.2 A Smart Micro-Grid Model 160
7.2.1 Notation 160
7.2.2 Power Network 161
7.2.3 Communication Network 162
7.2.4 Control Network 163
7.3 Inferring Private User Behaviour Based on Consumption Data 164
7.3.1 Inferential Attack Models 164
7.3.1.1 Passive Inference Attacks 164
7.3.1.2 Active Inference Attacks 166
7.4 Mitigating Privacy Inference 166
7.4.1 Inherent Distributed Differential Privacy (IDDP) 167
7.5 Related Work 168
7.6 Conclusion 169
References 169
Index 172