RL-MADP : Reinforcement Learning-based Misdirection Attack Prevention Technique for WSN

Mustafa, Iqra and Aslam, Shahzad and Aslam, Sheraz and Qureshi, Muhammad Bilal and Ashraf, Nouman and Mohsin, Syed Muhammad and Mustafa, Hasnain (2020) RL-MADP : Reinforcement Learning-based Misdirection Attack Prevention Technique for WSN. In: 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 :. 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 . Institute of Electrical and Electronics Engineers Inc., CYP, pp. 721-726. ISBN 9781728131290

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Abstract

Wireless Sensor Networks (WSNs) provide noteworthy advantages over conventional methods for various real-time applications, i.e., healthcare, temperature sensing, smart homes, homeland security, and environmental monitoring. However, limited resources, short life-time network constraints, and security vulnerabilities are the challenging issues in the era of WSNs. Besides, WSNs performance is susceptible to network anomalies, particularly to misdirection attacks. The above-mentioned issues pose our attentions to produce a security-aware application. In this work, therefore, we present a Reinforcement Learning (RL) algorithm for Misdirection Attack Detection and Prevention (RL-MADP) in WSNs. In our proposed approach, other than the flat architecture configuration for WSN, Markov Decision Process (MDP) from RL is considered. Where, each sensor node is fully aware of its environment. It is an online method and incurs minimal computation cost, and performs load-balancing with higher residual energy to prolong the network lifetime.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2020 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1711
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:16
Last Modified:	03 Aug 2023 04:50
URI:	http://repository-testing.wit.ie/id/eprint/4985

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