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Article

An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks

1
Institute of Computing, Kohat University of Science & Technology, Kohat 26000, Pakistan
2
Department of Communications and Networks Engineering, Prince Sultan University, Riyadh 11633, Saudi Arabia
3
Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak 27000, Pakistan
4
Department of Computer Science and Information Systems, Birla Institute of Technology & Science, Pilani, Hyderabad 500037, India
*
Author to whom correspondence should be addressed.
Academic Editor: Lorena Parra
Sensors 2022, 22(2), 410; https://doi.org/10.3390/s22020410
Received: 15 November 2021 / Revised: 5 December 2021 / Accepted: 10 December 2021 / Published: 6 January 2022
(This article belongs to the Special Issue Big Data Analytics in Internet of Things Environment)
Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems. View Full-Text
Keywords: WSNs; security; DDoS; flash crowd; Bayesian model WSNs; security; DDoS; flash crowd; Bayesian model
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MDPI and ACS Style

Khan, M.A.; Nasralla, M.M.; Umar, M.M.; Ghani-Ur-Rehman; Khan, S.; Choudhury, N. An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks. Sensors 2022, 22, 410. https://doi.org/10.3390/s22020410

AMA Style

Khan MA, Nasralla MM, Umar MM, Ghani-Ur-Rehman, Khan S, Choudhury N. An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks. Sensors. 2022; 22(2):410. https://doi.org/10.3390/s22020410

Chicago/Turabian Style

Khan, Muhammad Altaf, Moustafa M. Nasralla, Muhammad Muneer Umar, Ghani-Ur-Rehman, Shafiullah Khan, and Nikumani Choudhury. 2022. "An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks" Sensors 22, no. 2: 410. https://doi.org/10.3390/s22020410

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