A Trust-Based Framework and Deep Learning-Based Attack Detection for Smart Grid Home Area Network

Abstract

The Internet of things (IoT) can be used in our daily life. Home area network (HAN) is one of the applications of loT. The Smart Grid is an intelligent power network featured by its two-way flows of electricity andinformation. The integrated communication infrastructure allows Smart Grid systems to manage the operation of allconnected components to provide reliable and sustainable electricity supplies. The Home Area Network is a dedicatednetwork connecting devices in the home, as well as electrical vehicles. The HAN market is now emerging within thesmart grid sector to serve home with different solutions. However, at the same time, due to the dependence oninformation technology and the deep integration of electrical components and computing information in cyber space,the system might become increasingly vulnerable to cyber-attacks. Cyber-attacks have led to numerous incidents andhave been concerned by both power system operators and users. They can undermine or even completely disrupt thecontrol system of the power grid. This paper presents an approach to modelling and validating the secure HAN network.Here a novel Trust-Based Iterative Energy-Efficient Routing Protocol (TBIEERP) is suggested with a data encryptionscheme for secured data transmission in HAN. The Honeypot algorithm for encryption and decryption of data isemployed. Finally, to detect the intrusion a deep auto encoder was used for attack detection and to protect HAN againstcyber-attacks. The whole experimentation was carried out under Matlab environment. Thus, the techniques proposedproduce the most promising outcome over attack detection. The proposed method obtained better detection accuracycompared to the existing methods