Machine learning and multi-dimension features based adaptive intrusion detection in ICN

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dc.contributor.authorLi, Zhihao
dc.contributor.authorWu, Jun
dc.contributor.authorMumtaz, Shahid
dc.contributor.authorTaha, A-E M.
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2020-09-25T16:02:28Z
dc.date.available2020-09-25T16:02:28Z
dc.date.issued2020-07-27
dc.description.abstractAs a new network architecture, Information-Centric Networks (ICN) has great advantages in content distribution and can better meet our needs. But it faced with many threats unavoidably. There are four types of attack in ICN: naming related attacks, routing related attacks, caching related attacks and miscellaneous attacks. These attacks will undermine the availability of ICN, the confidentiality and privacy of data. In addition, routers store a large amount of content for the users' request, and it is necessary to protect these intermediate nodes. Since the styles of content stored in nodes are not the same, using a unified set of intrusion detection rules simply will cause a large number of false positives and false negatives. Therefore, every node should perform intrusion detection according to its own characteristics. In this paper, we propose an intrusion detection mechanism to alert for abnormal packets. We introduce a extensive solution using machine learning for attacks in ICN. Moreover, the nodes in this scheme can adapt to the external environment and intelligently detect packets. Simulation on the machine learning algorithm involved prove that the algorithm is effective and suitable for network packets.en_UK
dc.identifier.citationLi Z, Wu J, Mumtaz S, et al., (2020) Machine learning and multi-dimension features based adaptive intrusion detection in ICN. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 7-11 June 2020, Dublin, Irelanden_UK
dc.identifier.isbn978-1-7281-5089-5
dc.identifier.issn1938-1883
dc.identifier.urihttps://doi.org/10.1109/ICC40277.2020.9149250
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15851
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectICNen_UK
dc.subjectmachine learningen_UK
dc.subjectdefenseen_UK
dc.subjectintrusion detectionen_UK
dc.titleMachine learning and multi-dimension features based adaptive intrusion detection in ICNen_UK
dc.typeConference paperen_UK

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