Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing

Li, Fangyu; Shi, Yang; Shinde, Aditya; Ye, Jin; Song, Wen-Zhan

doi:10.1109/jiot.2019.2899492

Cited by 91 publications

(47 citation statements)

References 22 publications

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“…Both cyber and physical attacks are in high demand in smart agriculture. Li et al [121] proposed an auditing and analyticsbased IoT monitoring mechanism. The proposed mechanism uses disaggregation-aggregation architecture using the evaluation of the power usage of the system's sub-components.…”

Section: E Physical Countermeasuresmentioning

confidence: 99%

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Yang

Shu

Chen

et al. 2021

IEEE/CAA J. Autom. Sinica

252

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With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture.

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Section: E Physical Countermeasuresmentioning

confidence: 99%

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Yang

Shu

Chen

et al. 2021

IEEE/CAA J. Autom. Sinica

252

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show abstract

“…In the normal distribution power grids, the voltages and currents should be stable. If abnormal changes happen to ∆V n and ∆I np , an event can be detected based on certain thresholding methods [23], [24]. Here, instead of directly using the difference, we treat it as one dimension of the high-dimensional detection metrics matrix.…”

Section: B Feature Extractionmentioning

confidence: 99%

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids With PVs: An Online High-Dimensional Data-Driven Approach

Xie

Yang

et al. 2022

IEEE J. Emerg. Sel. Topics Power Electron.

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Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

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“…As data-driven methods do not require explicit physical models, they can cope with complex, complicated, and heterogeneous phenomena. There are many data-driven methods for the security issues, such as the geometrically designed residual filter [46], signal analytics based [152], generalized likelihood ratio [153], the cumulative sum (CUSUM) [154], leverage score [155], influential point selection [156], support vector machine (SVM) [121], Gaussian mixture model (GMM) [122], neural networks [123], machine learning [121], deep learning [157], and so on.…”

Section: Data-driven Cyber-attack Detection and Mitigationmentioning

confidence: 99%

Cyber–Physical Security of Powertrain Systems in Modern Electric Vehicles: Vulnerabilities, Challenges, and Future Visions

You-hai

Guo

Yang

et al. 2021

IEEE J. Emerg. Sel. Topics Power Electron.

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Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing

Cited by 91 publications

References 22 publications

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids With PVs: An Online High-Dimensional Data-Driven Approach

Cyber–Physical Security of Powertrain Systems in Modern Electric Vehicles: Vulnerabilities, Challenges, and Future Visions

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