Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91606
DC FieldValueLanguage
dc.contributorDepartment of Computingen_US
dc.creatorLi, WJen_US
dc.creatorMeng, WZen_US
dc.creatorWang, Yen_US
dc.creatorLi, Jen_US
dc.date.accessioned2021-11-17T08:51:28Z-
dc.date.available2021-11-17T08:51:28Z-
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10397/91606-
dc.descriptionInternational Conference on Wireless Algorithms, Systems, and Applications (WASA 2021), 25-27 June 2021, Nanjing, Chinaen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectWireless sensor networken_US
dc.subjectDistributed denial-of-service attacken_US
dc.subjectBlockchain technologyen_US
dc.subjectNetwork securityen_US
dc.subjectPacket filtrationen_US
dc.titleEnhancing blackslist-based packet filtration using blockchain in wireless sensor networksen_US
dc.typeConference Paperen_US
dc.identifier.spage624en_US
dc.identifier.epage635en_US
dc.identifier.volume12938 LNCSen_US
dc.identifier.doi10.1007/978-3-030-86130-8_49en_US
dcterms.abstractA wireless sensor network (WSN) consists of distributed sensors for monitoring network status and recording data, which is playing a major role in Internet of Things (IoT). This type of wireless network is driven by the availability of inexpensive and low-powered components. However, WSN is vulnerable to many kinds of attacks like Distributed Denial of Service (DDoS) due to its dispersed structure and unreliable transmission. In the literature, constructing a suitable distributed packet filter is a promising solution to help mitigate unwanted traffic.While how to ensure the integrity of exchanged data is a challenge as malicious internal node can share manipulated data to degrade the effectiveness of filtration. In this work, we design a blockchain-based blacklist packet filter with collaborative intrusion detection that can be deployed in WSNs. The blockchain technology is used to help build a robust blacklist for reducing unwanted traffic. In the evaluation, we investigate the performance of our filter with a real dataset and in a practical WSN environment. The results demonstrate that our proposed filter can enhance the robustness of blacklist generation.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2021, v. 12938 LNCS, p. 624-635en_US
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)en_US
dcterms.issued2021-
dc.relation.conferenceInternational Conference on Wireless Algorithms, Systems, and Applications [WASA]en_US
dc.identifier.eissn1611-3349en_US
dc.description.validate202111 bcwhen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1068-n06-
dc.identifier.SubFormID43873-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China (No. 61802080 and 61802077)en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2022-09-09en_US
Appears in Collections:Conference Paper
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Embargo End Date 2022-09-09
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