Show simple item record

dc.contributor.authorShire, R
dc.contributor.authorShiaeles, S
dc.contributor.authorBendiab, K
dc.contributor.authorGhita, B
dc.contributor.authorKolokotronis, N
dc.date.accessioned2023-04-20T09:59:23Z
dc.date.available2023-04-20T09:59:23Z
dc.date.issued2019-09-12
dc.identifier.isbn9783030308582
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://pearl.plymouth.ac.uk/handle/10026.1/20728
dc.description8 pages
dc.description.abstract

Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.

dc.format.extent65-76
dc.publisherSpringer International Publishing
dc.relation.ispartofInternet of Things, Smart Spaces, and Next Generation Networks and Systems
dc.subjectTraffic analysis
dc.subjectNeural network
dc.subjectBinary visualization
dc.subjectNetwork anomaly detection
dc.subjectIntrusion detection system
dc.titleMalware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation
dc.typechapter
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000565635700006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume11660
plymouth.publisher-urlhttp://dx.doi.org/10.1007/978-3-030-30859-9_6
plymouth.publication-statusPublished
plymouth.journalINTERNET OF THINGS, SMART SPACES, AND NEXT GENERATION NETWORKS AND SYSTEMS, NEW2AN 2019, RUSMART 2019
dc.identifier.doi10.1007/978-3-030-30859-9_6
plymouth.organisational-group|Plymouth
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics
dc.date.updated2023-04-20T09:59:14Z
dc.rights.embargodate10000-01-0
dc.identifier.eissn1611-3349
dc.rights.embargoperiodforever
rioxxterms.versionofrecord10.1007/978-3-030-30859-9_6


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV