A Deep Learning-Based Cyber-Physical Strategy to MitigateFalse Data Injection Attack in Smart Grids

Jin Wei and Gihan J. Mendis

Department of Electrical & Computer Engineering

04/12/2016

Outline

• Introduction

• Problem Setting

• Attack-Mitigation Model

• Deep Learning-Based Cyber-Physical Strategy

• Simulations

• Conclusions

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Introduction

• The attacks targeting availability, integrity, and confidentiality are identified as the major security issues for smart grids.

• We focus on one typical data integrity attack, false data injection attack.

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Introduction

• The tremendous amount of real-time data, such as PMU data, demands the appropriate data analysis and control techniques.

• Deep Belief Network (DBN) is coming to play a key role in providing big data predictive analytics solutions.

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Problem Setting• A two-tier hierarchical cyber-physical multi-agent control

framework for modeling the transient stability problem.

Agent 9Agent 7

Agent 3

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Problem Setting

• A two-tier hierarchical cyber-physical multi-agent control framework for modeling the transient stability problem.

– Inter-cluster (Tier-1): lead agents achieve frequency synchronization via cyber-physical couplings.

– Intra-cluster (Tier-2): multiple secondary agents achieve synchronization via strong physical couplings with a stabilized lead agent.

• Only lead agent PMU information is needed to ensure transient system stabilization in the face of a disturbance.

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Problem Setting

• A two-tier hierarchical cyber-physical multi-agent control framework for modeling the transient stability problem.

– Inter-cluster (Tier-1): lead agents achieve frequency synchronization via cyber-physical couplings.

– Intra-cluster (Tier-2): multiple secondary agents achieve synchronization via strong physical couplings with a stabilized lead agent.

• Only lead agent PMU information is needed to ensure transient system stabilization in the face of a disturbance.

How to defend against the false data injection attack on the lead agent PMUs?

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Attack-Mitigation Model

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Deep Learning-Based Cyber-Physical Strategy

• In the hierarchical framework, the states of the secondary agents can be treated as “noisy” version of those of the lead agents.

• The PMU data from the lead agents can be validated using the PMU data from the secondary agents.

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Deep Learning-Based Cyber-Physical Strategy

• The PDC works as an aggregator in the intra-cluster LAN to verify the trustworthiness of the lead agent’s PMU data.

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Deep Learning-Based Cyber-Physical Strategy

• The PDC works as an aggregator in the intra-cluster LAN to verify the trustworthiness of the lead agent’s PMU data.

– The PDC probes the PMU data from all of the secondary agents at a verification rate for learning the features of the PMU data of the cluster of agents and thus predicting the behavior pattern of the PMU data of the associated lead agent.

• Using Deep-learning Technique

– The PDC also probes the lead agent’s data at the same rate and measure the trustworthiness of a lead agent’s data by using the predicted behavior pattern.

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Deep Learning-Based Cyber-Physical Strategy

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Deep Learning-Based Cyber-Physical Strategy

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No. Input

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Deep Learning-Based Cyber-Physical Strategy

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Fig. 1: Conditional Deep Belief Network

Deep Learning-Based Cyber-Physical Strategy

• Considering that the 3-dimensional input data have real values, both of the CDBNs are trained by stacking one conditional GBRBM with three conditional BRBMs.

– For conditional GBRBM: Energy function.

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where

Deep Learning-Based Cyber-Physical Strategy

• Considering that the 3-dimensional input data have real values, both of the CDBNs are trained by stacking one conditional GBRBM with three conditional BRBMs.

– For conditional GBRBM: conditional probability distributions.

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where

Deep Learning-Based Cyber-Physical Strategy

• Considering that the 3-dimensional input data have real values, both of the CDBNs are trained by stacking one conditional GBRBM with three conditional BRBMs.

– For conditional BRBM: Energy function.

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where

Deep Learning-Based Cyber-Physical Strategy

• Considering that the 3-dimensional input data have real values, both of the CDBNs are trained by stacking one conditional GBRBM with three conditional BRBMs.

– For conditional BRBM: conditional probability distributions.

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where

Deep Learning-Based Cyber-Physical Strategy

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Deep Learning-Based Cyber-Physical Strategy

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Deep Learning-Based Cyber-Physical Strategy

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Simulations

Fig. 2: New England 39-bus power system

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Simulations

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Fig. 3: Normalized rotor frequencies and phase angles versus time without active control and proposed deep learning-based cyber-physical strategy

Simulations

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Simulations

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𝑈[−360o, 360o]

Simulations

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Fig. 4: Normalized rotor frequencies and phase angles versus time withactive control and without the proposed deep learning-based cyber-physical strategy in presence of random attack.

Simulations

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Simulations

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Simulations

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Fig. 5: Normalized rotor frequencies and phase angles versus time withactive control and the proposed deep learning-based cyber-physical strategy in presence of random attack.

Conclusions• We proposed the a deep learning-based approach to detect

and defend against the potential false data injection attack on the critical data for the application of maintaining the transient stability of real-time WAMS.

• Future work will examine a generalized class of threat models for which the approach is able to identify data corruption.

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Questions

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