@Article{cmc.2021.016260,
AUTHOR = {Jialin Wan, Muhammad Waqas, Shanshan Tu, Syed Mudassir Hussain, Ahsan Shah, Sadaqat Ur Rehman, Muhammad Hanif},
TITLE = {An Efficient Impersonation Attack Detection Method in Fog Computing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {1},
PAGES = {267--281},
URL = {http://www.techscience.com/cmc/v68n1/41852},
ISSN = {1546-2226},
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).},
DOI = {10.32604/cmc.2021.016260}
}