Cyber attack protection and control of microgrids

Rana, Masud; Li, Li; Su, Steven W.

doi:10.1109/jas.2017.7510655

Cited by 100 publications

(48 citation statements)

References 29 publications

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“…More instance of coordinated attacks on large power systems and its vulnerability assessment is provided in [19], [20]. In this regard, risk assessment alongwith control vulnerabilities is crucial since the modeling of coordinated attacks for microgrids can be easier owing to their small system size without significant security measures [21], [22].…”

Section: Introductionmentioning

confidence: 99%

A Stealth Cyber-Attack Detection Strategy for DC Microgrids

Sahoo

Mishra

Peng

et al. 2019

IEEE Trans. Power Electron.

201

132

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Section: Introductionmentioning

confidence: 99%

A Stealth Cyber-Attack Detection Strategy for DC Microgrids

Sahoo

Mishra

Peng

et al. 2019

IEEE Trans. Power Electron.

201

132

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“…The second step is to delete the multisensor WSN node that does not contain the incidence sensors. The third step is to compute the sensing time and transmitting time, according to Equation (10). Then, according to the time limit, an optimized WSN node that deals with the multitasking is reselected.…”

Section: Edge Computing-driven Sensing Strategy For Multiplementioning

confidence: 99%

“…With the development of agriculture, data collection becomes the main key step by WSN [8,9]. At the same time, cyberattacks of WSN are another hot topic for the researchers [10,11]. Therefore, more and more studies focus on the topic for different perspective, especially network attacks, data collection.…”

Section: Introductionmentioning

confidence: 99%

Edge Computing-Enabled Wireless Sensor Networks for Multiple Data Collection Tasks in Smart Agriculture

Zhu

Chu

et al. 2020

Journal of Sensors

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At present, precision agriculture and smart agriculture are the hot topics, which are based on the efficient data collection by using wireless sensor networks (WSNs). However, agricultural WSNs are still facing many challenges such as multitasks, data quality, and latency. In this paper, we propose an efficient solution for multiple data collection tasks exploiting edge computing-enabled wireless sensor networks in smart agriculture. First, a novel data collection framework is presented by merging WSN and edge computing. Second, the data collection process is modeled, including a plurality of sensors and tasks. Next, according to each specific task and correlation between task and sensors, on the edge computing server, a double selecting strategy is established to determine the best node and sensor network that fulfills quality of data and data collection time constraints of tasks. Furthermore, a data collection algorithm is designed, based on set values for quality of data. Finally, a simulation environment is constructed where the proposed strategy is applied, and results are analyzed and compared to the traditional methods. According to the comparison results, the proposal outperforms the traditional methods in metrics.

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“…Based on the similarity level (by cosine similarity measure) and deviation level (by chi-square test) the FDI attack was detected. Recursive systematic convolutional (RSC) was combined with the Kalman filter in order to detect FDI attacks in SGs [29]. In such a system, the RSC code was employed to handle impairments in the system states and the Kalman filter was used for SE.…”

Section: Related Work On Fdi Attack Detectionmentioning

confidence: 99%

T2S2G: A Novel Two-Tier Secure Smart Grid Architecture to Protect Network Measurements

Aziz

Jin²,

Abdulqadder³

et al. 2019

Energies

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False data injection (FDI) attacks are a major security threat to smart grid (SG) communication systems. In FDI attacks, the attacker has the ability of modifying the measurements transmitted by smart grid entities such as smart meters, buses, etc. Many solutions have been proposed to mitigate FDI attacks in the SG. However, most of these solutions rely on centralized state estimation (SE), which is computationally expensive. To engulf this problem in FDI attack detection and to improve security of SGs, this paper proposes novel two-tier secure smart grid (T2S2G) architecture with distributed SE. In T2S2G, measurement collection and SE are involved in first tier while compromised meter detection is involved in second tier. Initially the overall SG system is divided into four sections by the weighted quad tree (WQT) method. Each section is provided with an aggregator, which is responsible to perform SE. Measurements from power grids are collected by remote terminal units (RTUs) and encrypted using a parallel enhanced elliptic curve cryptography (PEECC) algorithm. Then the measurements are transmitted to the corresponding aggregator. Upon collected measurements, aggregator estimates state using the amended particle swarm optimization (APSO) algorithm in a distributed manner. To verify authenticity of aggregators, each aggregator is authenticated by a logical schedule based authentication (LSA) scheme at the control server (CS). In the CS, fuzzy with a neural network (FNN) algorithm is employed for measurements classification. Our proposed T2S2G shows promising results in the following performance metrics: Estimation error, number of protected measurements, detection probability, successful detection rate, and detection delay.

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Cyber attack protection and control of microgrids

Cited by 100 publications

References 29 publications

A Stealth Cyber-Attack Detection Strategy for DC Microgrids

A Stealth Cyber-Attack Detection Strategy for DC Microgrids

Edge Computing-Enabled Wireless Sensor Networks for Multiple Data Collection Tasks in Smart Agriculture

T2S2G: A Novel Two-Tier Secure Smart Grid Architecture to Protect Network Measurements

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