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

Mustafa, İqra; Aslam, Sheraz; Qureshi, Muhammad Bilal; Ashraf, Nouman; Aslam, Shahzad; Mohsin, Syed Muhammad; Mustafa, Hasnain

doi:10.1109/iwcmc48107.2020.9148445

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Mustafa et al, 2020 100 suggests an RL algorithm (Reinforcement Learning) for Misdirection Attack Detection and Prevention (RLMADP) in WSNs. In addition to the level architecture arrangement for WSN, the MDP (Markov Decision Process) from RL is estimated in their proposed approach.…”

Section: Network Layer Attacksmentioning

confidence: 99%

“…In a Sybil assault, an adversary hacks a network node, and the compromised node pretends to be numerous nodes using false identities. 100 The effect of the Sybil attack on the performance of IDS in WSN was considered by Jamshidi et al, 2019 117 a novel Sybil attack model for cluster based WSNs such as LEACH is suggested. The given method is built on cluster head node collaboration and RSSI (Received Signal Strength Indicator).…”

Section: Wsn Attack Typesmentioning

confidence: 99%

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Wireless sensor network security: A recent review based on state-of-the-art works

Faris

Mahmud

Salleh

et al. 2023

International Journal of Engineering Business Management

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Wireless sensor networks (WSNs) are a major part of the telecommunications sector. WSN is applied in many aspects, including surveillance battlefields, patient medical monitoring, building automation, traffic control, environmental monitoring, and building intrusion monitoring. The WSN is made up of a vast number of sensor nodes, which are interconnected through a network. However, despite the growing usage of applications that rely on WSNs, they continue to suffer from restrictions, such as security issues and limited characteristics due to low memory and calculation power. Security issues lead to a lack of communication between sensors, wasting more energy. The need for efficient solutions has increased, especially with the rise of the Internet of Things, which relies on the effectiveness of WSNs. This review focuses on security issues by reviewing and addressing diverse types of WSN assaults that happened on each layer of the WSN that were published in security issues in the previous 3 years. As a consequence, this paper gives a taxonomy of security threats for each layer and different algorithmic solutions that numerous researchers who seek to counter this attack have explored. This study also presents a framework for constructing an intrusion detection system in the WSN by emphasising the drawbacks of each approach suggested by researchers to defend against specific forms of assault. In order to diminish the impact of this attack, this summary shows which attacks the majority of researchers have dealt with as well as which ones they have not yet addressed in their academic work.

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Section: Network Layer Attacksmentioning

confidence: 99%

Section: Wsn Attack Typesmentioning

confidence: 99%