Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning

Sengan, Sudhakar; Subramaniyaswamy,; Indragandhi,; Velayutham, Priya; Ravi, Logesh

doi:10.1016/j.compeleceng.2021.107211

Cited by 77 publications

(32 citation statements)

References 15 publications

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“…Solutions: To deal with the security threats that target the confidentiality of smart grids, several methods have been proposed [90]. For example, one can use data encryption against password theft attacks [91].…”

Section: ) Smart Gridsmentioning

confidence: 99%

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

et al. 2022

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Communication systems and networks are evolving as an integral part of not only of our everyday life but also as a part of the industry, fundamental infrastructures, companies, etc. Current directions and concepts, such as the Internet of Things (IoT), promise enhanced quality of life, greater business opportunities, cost-effective manufacturing, and efficient operation management through ubiquitous connectivity and deployment of smart physical objects. IoT networks are able to collect, preprocess, and transmit a huge amount of data. A considerable portion of this data is security-and privacycritical data, which makes IoT networks a tempting option for attackers. Given that these networks deal with the actual aspects of our lives and fundamental infrastructures (e.g. smart grids), cybersecurity in such networks is crucial. The large scale of these networks and their unique characteristics and complexity bring further vulnerabilities. In this study we give an introduction to IoT, focusing on the IoT application layer, security requirements, threats, and countermeasures in this layer, and some of the open issues and future research lines.

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Section: ) Smart Gridsmentioning

confidence: 99%

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

et al. 2022

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“…They have decided the intermittent qualities of different stacking, transmission way and their effect on the vibration abundancy. In [15] contends that Fourier examination gives an unfortunate portrayal of signs very much confined in time in light of the fact that the FFT expects to be a fixed sign and contains no data on when an occasion happened. The article involved the WT for of separating time recurrence data from the vibration signal.…”

Section: Related Workmentioning

confidence: 99%

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

Awasthi¹,

Mahesh²,

Joshi³

et al. 2022

Int. j. commun. netw. inf. secur.

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The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model.

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“…There are also various other applications that are used in this area, such as Microsoft Teams, Adobe Connect, and Livestorm. According to a report released by Datanyze, the world's leading provider of technography, the three most used VCA tools in 2020 were Zoom, GoToWebinar, and Cisco Webex [14]. This process was carried out through the review of various publications that were published in various digital libraries, such as ScienceDirect, Springerlink, and IEEE Xplore.…”

Section: Advantages Of Technologymentioning

confidence: 99%

Cyber Security Threats to Educational Institutes: A Growing Concern for the New Era of Cybersecurity

Jawaid¹

2022

Preprint

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Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.

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Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning

Cited by 77 publications

References 15 publications

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

Cyber Security Threats to Educational Institutes: A Growing Concern for the New Era of Cybersecurity

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