Recursive-clustering-based approach for denial of service (DoS) attacks in wireless sensors networks

SUMMARYThis paper presents a novel approach for detecting denial of service attacks. In particular, the concern is on the sleeping deprivation attacks such as the malicious nodes that use flooding technique. Our approach is based on wireless sensor network (WSN) clustering. It consists in recursivel...

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Vydáno v:International journal of communication systems Ročník 28; číslo 2; s. 309 - 324
Hlavní autoři: Fouchal, S., Mansouri, D., Mokdad, L., Iouallalen, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester Blackwell Publishing Ltd 25.01.2015
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Témata:
Algorithms
Clustering
Computer Science
Denial of service attacks
DoS attack
FFUCA
Flooding
LEACH
Mathematical models
Remote sensors
Sensors
Wireless networks
WSN
ISSN:1074-5351, 1099-1131
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Shrnutí:SUMMARYThis paper presents a novel approach for detecting denial of service attacks. In particular, the concern is on the sleeping deprivation attacks such as the malicious nodes that use flooding technique. Our approach is based on wireless sensor network (WSN) clustering. It consists in recursively clustering sensors until a required granularity (chosen by the expert) is achieved. We apply our approach with two different clustering algorithms. Indeed, we use the common clustering WSN algorithm Low Energy Algorithm Adaptive Clustering Hierarchy and the general clustering method Fast and Flexible Unsupervised Clustering Algorithm (FFUCA) based on ultrametric properties. We discuss the behavior of the approach with the two algorithms. Also, we present numerical results that show the efficiency of recursive clustering using the FFUCA algorithm. Copyright © 2013 John Wiley & Sons, Ltd. This paper presents a novel approach for detecting denial of service attacks in wireless sensor networks. The concern here is on the sleeping deprivation attacks such as malicious sensors that use flooding technique. The idea consists in clustering sensors recursively until a required granularity is achieved. We use here a generic clustering algorithm named Fast and Flexible Unsupervised Clustering Algorithm based on ultrametric properties. We present numerical results that show the efficiency of our approach using the Fast and Flexible Unsupervised Clustering Algorithm.
Bibliografie:ark:/67375/WNG-B6XTS8WJ-Z
ArticleID:DAC2670
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ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1074-5351
1099-1131
DOI:10.1002/dac.2670