Open Access

ARTICLE

Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network

by Abdullah Musaed Alkhiari¹, Shailendra Mishra^2,*, Mohammed AlShehri¹

1 Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia
2 Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia

* Corresponding Author: Shailendra Mishra. Email:

Computers, Materials & Continua 2022, 70(2), 2149-2169. https://doi.org/10.32604/cmc.2022.019562

Received 17 April 2021; Accepted 20 May 2021; Issue published 27 September 2021

Abstract

Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all smart meters are first registered in the blockchain, and then the blockchain generates a secret key. The blockchain verifies the user ID and the secret key during authentication matches the one authorized to access the network. The secret key is shared during transmission through secure quantum key distribution (SQKD). The second phase is the lightweight data encryption, for which we use a lightweight symmetric encryption algorithm, named Camellia. The third phase is the multi-constraint-based edge selection; the data are transmitted to the control center through the edge server, which is also authenticated by blockchain to enhance the security during the data transmission. We proposed a perfect matching algorithm for selecting the optimal edge. The fourth phase is a dual intrusion detection system which acts as a firewall used to drop irrelevant packets, and data packets are classified into normal, physical errors and attacks, which is done by Double Deep Q Network (DDQN). The last phase is optimal user privacy management. In this phase, smart meter updates and revocations are done, for which we proposed Forensic based Investigation Optimization (FBI), which improves the security of the smart grid network. The simulation is performed using network simulator NS3.26, which evaluates the performance in terms of computational complexity, accuracy, false detection, and false alarm rate. The proposed BLIDSE model effectively mitigates cyber-attacks, thereby contributing to improved security in the network.

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