Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid

Abstract The national security, economy, and healthcare heavily rely on the reliable distribution of electricity. The incorporation of communication technologies and sensors in the power structures, recognized as the smart grid which revolutionizes the model of the production, distribution, monitoring, and control of the electricity. To realize the applicability of smart grid, several issues need to be addressed. Securing the smart grid is a very challenging task and a pressing issue. In this article, a secure demand-side management (DSM) engine is proposed using machine learning (ML) for the Internet of Things (IoT)-enabled grid. The proposed DSM engine is responsible to preserve the efficient utilization of energy based on priorities. A specific resilient model is proposed to control intrusions in the smart grid. The resilient agent predicts the dishonest entities using the ML classifier. Advanced energy management and interface controlling agents are proposed to process energy information to optimize energy utilization. The efficient simulation is executed to test the efficiency of the proposed scheme. The analysis results reveal that the projected DSM engine is less vulnerable to the intrusion and effective enough to reduce the power utilization of the smart grid.

[1]  Milos Manic,et al.  Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions , 2019 .

[2]  Àngela Nebot,et al.  Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques , 2015 .

[3]  Zhong Fan,et al.  Overview of demand management in smart grid and enabling wireless communication technologies , 2012, IEEE Wireless Communications.

[4]  Panagiotis G. Sarigiannidis,et al.  Securing the Smart Grid: A Comprehensive Compilation of Intrusion Detection and Prevention Systems , 2019, IEEE Access.

[5]  Paras Mandal,et al.  Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[6]  Gwanggil Jeon,et al.  Energy-harvesting based on internet of things and big data analytics for smart health monitoring , 2017, Sustain. Comput. Informatics Syst..

[7]  Kui Wu,et al.  A Machine Learning Approach to Meter Placement for Power Quality Estimation in Smart Grid , 2016, IEEE Transactions on Smart Grid.

[8]  Syed Hassan Ahmed,et al.  NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks , 2018, The Journal of Supercomputing.

[9]  Sadia Din,et al.  Exploring Deep Learning Models for Overhead View Multiple Object Detection , 2020, IEEE Internet of Things Journal.

[10]  Biplab Sikdar,et al.  Privacy-Aware Authenticated Key Agreement Scheme for Secure Smart Grid Communication , 2019, IEEE Transactions on Smart Grid.

[11]  Syed Ali Hassan,et al.  Machine Learning in IoT Security: Current Solutions and Future Challenges , 2019, IEEE Communications Surveys & Tutorials.

[12]  Kijoon Chae,et al.  Secure and structured IoT smart grid system management , 2017, Int. J. Web Grid Serv..

[13]  Francesco Palmieri,et al.  Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification , 2016, Soft Computing.

[14]  Tanveer Ahmad,et al.  A review on renewable energy and electricity requirement forecasting models for smart grid and buildings , 2020 .

[15]  Won-Hwa Hong,et al.  Constrained application for mobility management using embedded devices in the Internet of Things based urban planning in smart cities , 2019 .

[16]  Fahim Arif,et al.  Real-time data processing scheme using big data analytics in internet of things based smart transportation environment , 2019, J. Ambient Intell. Humaniz. Comput..

[17]  Jia-Lun Tsai,et al.  Secure Anonymous Key Distribution Scheme for Smart Grid , 2016, IEEE Transactions on Smart Grid.

[18]  Vigna Kumaran Ramachandaramurthy,et al.  Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues , 2020 .

[19]  Awais Ahmad,et al.  Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development , 2020 .

[20]  Arun Kumar Sangaiah,et al.  A Robust Features-Based Person Tracker for Overhead Views in Industrial Environment , 2018, IEEE Internet of Things Journal.

[21]  Zhiguo Ding,et al.  I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis , 2020, IEEE Transactions on Network Science and Engineering.

[22]  Zita Vale,et al.  Flexibility management model of home appliances to support DSO requests in smart grids , 2020, Sustainable Cities and Society.

[23]  Zhiwei Wang,et al.  A Secure and Efficient Framework to Read Isolated Smart Grid Devices , 2017, IEEE Transactions on Smart Grid.

[24]  Eklas Hossain,et al.  Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review , 2019, IEEE Access.

[25]  Burak Kantarci,et al.  A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities , 2019, Sustainable Cities and Society.

[26]  Bruno Sinopoli,et al.  Challenges and Opportunities: Cyber-Physical Security in the Smart Grid , 2018, Smart Grid Control.

[27]  Andrea M. Tonello,et al.  Smart Grid Monitoring Using Power Line Modems: Anomaly Detection and Localization , 2018, IEEE Transactions on Smart Grid.

[28]  Gwanggil Jeon,et al.  Efficient topview person detector using point based transformation and lookup table , 2019, Comput. Commun..

[29]  Dharmendra Yadav,et al.  Security risk analysis approach for smart grid , 2018 .

[30]  Fadi Al-Turjman,et al.  IoT-enabled smart grid via SM: An overview , 2019, Future Gener. Comput. Syst..

[31]  Hamid Sharif,et al.  A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges , 2013, IEEE Communications Surveys & Tutorials.

[32]  Kibet Langat,et al.  Cyber security challenges for IoT-based smart grid networks , 2019, Int. J. Crit. Infrastructure Prot..

[33]  Xiaojiang Du,et al.  A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security , 2018, IEEE Communications Surveys & Tutorials.

[34]  Zhiyuan Tan,et al.  Urban data management system: Towards Big Data analytics for Internet of Things based smart urban environment using customized Hadoop , 2019, Future Gener. Comput. Syst..

[35]  Malik Qasaimeh,et al.  Authentication techniques in smart grid: a systematic review , 2019 .

[36]  Vinay Chamola,et al.  Blockchain in Smart Grids: A Review on Different Use Cases , 2019, Sensors.

[37]  Marco Anisetti,et al.  Reliability and capability based computation offloading strategy for vehicular ad hoc clouds , 2019, Journal of Cloud Computing.

[38]  Fadi Al-Turjman,et al.  Software-defined wireless sensor networks in smart grids: An overview , 2019, Sustainable Cities and Society.