Open Access

ARTICLE

An Efficient Impersonation Attack Detection Method in Fog Computing

Jialin Wan¹, Muhammad Waqas^1,2, Shanshan Tu^1,*, Syed Mudassir Hussain³, Ahsan Shah², Sadaqat Ur Rehman⁴, Muhammad Hanif²

1 Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan
3 Department of Electronics Engineering, FICT, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, 87300, Pakistan
4 Department of Computer Science, Namal Institute, Mianwali, 42200, Pakistan

* Corresponding Author: Shanshan Tu. Email:

Computers, Materials & Continua 2021, 68(1), 267-281. https://doi.org/10.32604/cmc.2021.016260

Received 28 December 2020; Accepted 28 January 2021; Issue published 22 March 2021

Abstract

Fog computing paradigm extends computing, communication, storage, and network resources to the network’s edge. As the fog layer is located between cloud and end-users, it can provide more convenience and timely services to end-users. However, in fog computing (FC), attackers can behave as real fog nodes or end-users to provide malicious services in the network. The attacker acts as an impersonator to impersonate other legitimate users. Therefore, in this work, we present a detection technique to secure the FC environment. First, we model a physical layer key generation based on wireless channel characteristics. To generate the secret keys between the legitimate users and avoid impersonators, we then consider a Double Sarsa technique to identify the impersonators at the receiver end. We compare our proposed Double Sarsa technique with the other two methods to validate our work, i.e., Sarsa and Q-learning. The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER).

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