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Enabling Efficient and Malicious Secure data Aggregation in Smart Grid With false data detection

IEEE TRANSACTIONS ON SMART GRID 2024年第2期15卷 2203-2213页

作者： Pang, Haolin He, Kai Fu, Youcai Liu, Jia-Nan Liu, Xueqiao Tan, Wuzheng Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China Guangzhou Fongwell Data Ltd Co Guangzhou 511400 Peoples R China Pazhou Lab Guangzhou 510000 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China

As the next-generation power grid, the smart grid has significantly improved dependability, flexibility, and efficiency compared with the traditional power grid. However, due to increasingly diverse application requirements, it faces challenges on balancing data privacy, efficiency, and robustness. In this paper, we present a fog computing-based smart grid model. In addition, based on the proposed model, we construct an efficient and privacy-preserving scheme that supports malicious secure smart grid usage data aggregation communication. To our best knowledge, this is the first concrete smart grid solution that concurrently achieves secure aggregation communication, data privacy, and data robustness (e.g., false data detection). Specifically, benefiting from Boolean/Arithmetic secret-sharing methods, our proposed scheme allows home users to report their electricity usage data to the cloud and fogs securely. Besides, a false data detection protocol is proposed to resist false data injection attacks launched by malicious home users. Theoretical analysis and experimental implementation show that our scheme efficiently achieves data security, anonymity, and robustness.

关键词： Smart grids Cloud computing Computational modeling Smart meters data aggregation Robustness data privacy Smart grid false data detection robustness privacy-preserving malicious secure

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Deep Semi-Supervised Learning Method for false data detection Against Forgery and Concealing of Faults in Cyber-Physical Power Systems

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IEEE TRANSACTIONS ON SMART GRID 2024年第1期15卷 944-958页

作者： Wang, Guoteng Wang, Chongyu Shahidehpour, Mohammad Lin, Wei Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China IIT Galvin Ctr Elect Innovat Chicago IL 60616 USA King Abdulaziz Univ ECE Dept Jeddah 21589 Saudi Arabia

This paper proposes a deep semi-supervised numerical false data detection (DSS-NFDD) method, where hidden data in labeled and unlabeled datasets are leveraged simultaneously, to detect the false data in CPPS. Two types of false data are considered in this study: one is the forged fault data, which makes operators mistakenly believe that there is a fault in their operating system;the other is the false data used to conceal actual faults. First, a data dimension reduction method is proposed based on the PageRank algorithm to avoid the excessive noise caused by high-dimension data. Then, a semi-supervised deep learning framework is established to detect false data samples, which consists of two parts: one is the priori estimation module, and the other is a false data scoring network. A novel concentration loss function is presented for training the false data scoring network, which minimizes the impacts of noise pollution and sample bias. Next, a false feature location method is proposed to help human operators eliminate anomalies. Finally, the effectiveness and the superiorities of the proposed DSS-NFDD method are verified by analyzing the simulation results for the IEEE-39 bus and IEEE-118 bus systems.

关键词： Cyber-physical power systems false data detection semi-supervised learning false data scoring network

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Quick and Accurate false data detection in Mobile Crowd Sensing

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1339-1352页

作者： Li, Xiaocan Xie, Kun Wang, Xin Xie, Gaogang Xie, Dongliang Li, Zhenyu Wen, Jigang Diao, Zulong Wang, Tian Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518000 Peoples R China Purple Mt Lab Nanjing 211111 Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Taobao Com Beijing 100102 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Quanzhou 362021 Peoples R China

The attacks, faults, and severe communication/system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and false Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.

关键词： Sparse matrices Sensors Matrix decomposition Monitoring Principal component analysis Robustness Wireless sensor networks Matrix separation false data detection mobile crowd sensing

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A Novel false Measurement data detection Mechanism for Smart Grids

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ENERGIES 2023年第18期16卷 6614页

作者： Shahid, Muhammad Awais Ahmad, Fiaz Nawaz, Rehan Khan, Saad Ullah Wadood, Abdul Albalawi, Hani Air Univ Dept Elect & Comp Engn Islamabad 44230 Pakistan Air Univ Islamabad Dept Elect Engn Kamra Campus Kamra 43570 Pakistan Univ Tabuk Fac Engn Dept Elect Engn Tabuk 47913 Saudi Arabia Univ Tabuk Renewable Energy & Energy Efficiency Ctr REEEC Tabuk 71491 Saudi Arabia

With the growing cyber-infrastructure of smart grids, the threat of cyber-attacks has intensified, posing an increased risk of compromised communication links. Of particular concern is the false data injection (FDI) attack, which has emerged as a highly dangerous cyber-attack targeting smart grids. This paper addresses the limitations of the variable dummy value model proposed in the authors previous work and presents a novel defense methodology called the nonlinear function-based variable dummy value model for the AC power flow network. The proposed model is evaluated using the IEEE 14-bus test system, demonstrating its effectiveness in detecting FDI attacks. It has been shown that previous detection techniques are unable to detect FDI attacks, whereas the proposed method is shown to be successful in the detection of such attacks, guaranteeing the security of the smart grid's measurement infrastructure.

关键词： cyber-physical systems power system state estimation false data injection attacks false data detection cyber security

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Quick and Accurate false data detection in Mobile Crowd Sensing

Quick and Accurate False Data Detection in Mobile Crowd Sens...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Xie, Kun Li, Xiaocan Wang, Xin Xie, Gaogang Xie, Dongliang Li, Zhenyu Wen, Jigang Diao, Zulong Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Beijing Univ Posts & Telecommun State key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728105154

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and false Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection perliwmance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

关键词： Matrix Separation false data detection Mobile Crowd Sensing

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Impact of false data detection on Cloud Hosted Linear State Estimator Performance

Impact of False Data Detection on Cloud Hosted Linear State ...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Chakati, Vinaya Pore, Madhurima Banerjee, Ayan Pal, Anamitra Gupta, Sandeep K. S. Arizona State Univ Tempe AZ 85281 USA

ISBN: (纸本)9781538677032

Linear state estimation (LSE) is used to compute the complex voltages of a power system using measurements obtained only from phasor measurement units (PMUs). With the continued addition of PMUs into the grid, classical LSE solvers would have to handle large sets of high-speed data. Furthermore, security threats in the form of false data injection (FDI) attacks must also be considered in the design, which will considerably add to the computational overhead of LSE solvers. Although installing additional computation and communication hardware is a possible solution, such a solution would incur substantial infrastructure and operation costs. In this paper, we explore the design of a cost-effective and scalable cloud hosted LSE (CLSE) solver that also has false data detection (FDD). The proposed CLSE-FDD application exploits GPU parallel processing capabilities for mitigating the performance overhead of FDD to match the operation speed of classical LSE solvers. Results indicate that the GPU based CLSE-FDD application can easily scale in excess of 1,500 PMU installations

关键词： Terms Cloud Computing false data detection GPU Linear State Estimation Phasor Measurement Unit (PMU)

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false data Detector for Electrical Vehicles Temporal-Spatial Charging Coordination Secure Against Evasion and Privacy Adversarial Attacks

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第4期21卷 2027-2044页

作者： Shafee, Ahmed Mahmoud, Mohamed Bruce, J. W. Srivastava, Gautam Balamsh, Abdullah Aljohani, Abdulah J. Adams State Univ Dept Comp Sci Alamosa CO 81101 USA Helwan Univ Dept Comp Engn Cairo 4034572 Egypt Tennessee Technol Univ Dept Elect & Comp Engn Cookeville 38505 TN USA Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 404 Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut 11022801 Lebanon King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

As the number of electric vehicles on roads significantly increases, spatial-temporal charging coordination mechanisms have been introduced for balancing charging demand and energy supply. However, electric vehicles could send false data, such as state-of-charge (SoC), to the charging coordination mechanism for gaining high charging priority illegally. Machine Learning models can be used to detect false data. However, in our application the detector is trained on a dataset that contains sensitive information, such as the locations and SoC values of the electric vehicles. Therefore, attackers could launch adversarial attacks against the detector, such as membership inference and model inversion, for revealing sensitive information on the drivers whose data are used to train the detector. Furthermore, attackers could launch evasion attacks against the detector by computing false SoC values that are classified benign by the detector. Addressing the three attacks simultaneously makes the problem more complicated because a countermeasure to one attack may degrade the model's accuracy and unintentionally make the model more susceptible to other attacks. Accordingly, in this article, we propose a deep-learning training approach for false data detector in spatial-temporal charging coordination. Our approach can deal with the tradeoffs and balance the detector's accuracy and robustness against the adversarial attacks. Specifically, our approach combines three techniques, including mimic learning, dropout, and differential privacy, in a certain way that makes the detector highly accurate in detecting false data and also robust against adversarial attacks. To validate our approach, we have conducted a set of experiments and the given results demonstrate the robustness and accuracy of our detector.

关键词： Detectors data models Computational modeling Training Electric vehicles Robustness Threat modeling Security privacy preservation false data detection charging coordination electric vehicles and smart grid

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Efficient One-Class false data Detector Based on Deep SVDD for Smart Grids

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ENERGIES 2023年第20期16卷 7069页

作者： Habbak, Hany Mahmoud, Mohamed Fouda, Mostafa M. Alsabaan, Maazen Mattar, Ahmed Salama, Gouda I. Metwally, Khaled Mil Tech Coll Dept Comp Engn & AI Cairo 11766 Egypt Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Idaho State Univ Coll Sci & Engn Dept Elect & Comp Engn Pocatello ID 83209 USA Ctr Adv Energy Studies CAES Idaho Falls ID 83401 USA King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11451 Saudi Arabia

In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm;(2) the need for knowledge on the malicious data to compute good detection thresholds;and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description (DSVDD) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD-based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve (AUC) when compared to existing state-of-the-art detectors.

关键词： false data detection electricity theft smart meters automatic metering infrastructure smart power grid deep-SVDD one-class classification

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Blockchain-Based Distributed Cooperative Control Algorithm for WSN Monitoring 15th

Blockchain-Based Distributed Cooperative Control Algorithm f...

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15th International Conference on Distributed Computing and Artificial Intelligence (DCAI)

作者： Casado-Vara, Roberto Univ Salamanca BISITE Digital Innovat Hub Edificio Multiusos IDi Salamanca 37007 Spain

ISBN: (纸本)9783319996080

The management of heterogeneous distributed sensor networks requires new solutions to address the problem of data quality and false data detection in Wireless Sensor Networks (WSN). In this paper, we present a nonlinear cooperative control algorithm based on game theory and blockchain. Here, a new model is proposed for the automatic processing and management of data in heterogeneous distributed wireless sensor networks stored in a blockchain. We apply our algorithm for improving temperature data quality in indoor surfaces.

关键词： Wireless Sensors Network Blockchain Game theory Nonlinear models and systems data quality false data detection Nonlinear cooperative control

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Deterministic En-Route Filtering of false Reports: A Combinatorial Design Based Approach

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IEEE ACCESS 2018年 6卷 74494-74505页

作者： Kumar, Alok Pais, Alwyn R. Natl Inst Technol Karnataka Surathkal Dept Comp Sci & Engn Informat Secur Res Lab Mangalore 575025 India

Wireless sensor networks are an easy target for report fabrication attack, where compromised sensor nodes can be used by an adversary to flood the network with bogus/false reports. En-route filtering is a mechanism where intermediate forwarding nodes identify and drop false reports while they are being forwarded toward the sink. Most of the existing en-route filtering schemes are probabilistic, where sensor nodes in each cell share secret keys with a fixed probability with intermediate nodes. Thus, forwarded reports are verified probabilistically by intermediate nodes, because of which false reports can travel several hops before being dropped. Few deterministic en-route filtering schemes have also been proposed in the literature, but all such schemes require a source to send the reports through a fixed path to reach the sink. In this paper, we propose a novel deterministic en-route filtering scheme based on a combinatorial design to overcome the above-mentioned limitations of the existing schemes. The use of combinatorial design-based keys ensures direct communication between all the sensor nodes while maintaining low key storage overhead in the network. We provide a comprehensive analysis of the proposed scheme. The proposed scheme notably performs better than the existing schemes in terms of the expected filtering position of false reports. Furthermore, the proposed scheme improves data authenticity in the network and is more buoyant to selective forwarding and report disruption attacks.

关键词： Combinatorial design false data detection en-route filtering wireless sensor networks (WSNs)

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