Passive Encrypted IoT Device Fingerprinting with Persistent Homology

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dc.contributor.authorCollins, Joseph R.
dc.contributor.authorIorga, Michaela
dc.contributor.authorCousin, Dmitry
dc.contributor.authorChapman, David
dc.date.accessioned2021-01-04T19:37:35Z
dc.date.available2021-01-04T19:37:35Z
dc.date.issued2020-12-09
dc.descriptionTopological Data Analysis and Beyond Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.en_US
dc.description.abstractInternet of things (IoT) devices are becoming increasingly prevalent. These devices can improve quality of life, but often present significant security risks to end users. In this work we present a novel persistent homology based method for the fingerprinting of IoT traffic. Traditional passive device fingerprinting methods directly inspect the packet attributes or contents within the captured traffic. But techniques to fingerprint devices based on inter-packet arrival time (IAT) are an important area of research, as this feature is available even in encrypted traffic. We demonstrate that Topological Data Analysis (TDA) using persistent homology over IAT packet windows is a viable approach to obtain discriminative features for device fingerprinting. The clique complex construction and weighting function we present are efficient to compute and robust to shifts of the packet window. The1-dimensional homology is calculated over the resulting filtered clique complex. We obtain competitive accuracy of 95.34% on the UNSW IoT dataset by using a convolutional neural network to classify over the corresponding persistence images.en_US
dc.description.urihttps://openreview.net/forum?id=BXGqPm6nKgPen_US
dc.format.extent2 filesen_US
dc.genreconference papers and proceedings postprintsen_US
dc.identifierdoi:10.13016/m2g4ty-bpkq
dc.identifier.citationCollins, Joseph R.; Iorga, Michaela; Cousin, Dmitry; Chapman, David; Passive Encrypted IoT Device Fingerprinting with Persistent Homology; Topological Data Analysis and Beyond Workshop (2020); https://openreview.net/forum?id=BXGqPm6nKgPen_US
dc.identifier.urihttp://hdl.handle.net/11603/20274
dc.language.isoen_USen_US
dc.publisherOpenReviewen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titlePassive Encrypted IoT Device Fingerprinting with Persistent Homologyen_US
dc.typeTexten_US

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