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AuthorTabassum A.
AuthorErbad A.
AuthorMohamed A.
AuthorGuizani M.
Available date2022-04-21T08:58:23Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3051530
URIhttp://hdl.handle.net/10576/30077
AbstractExisting techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results. 2013 IEEE.
SponsorQatar University
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectIntrusion detection
Learning systems
Network security
Privacy by design
Classification accuracy
Distributed intrusion detection
Extraction techniques
Incremental learning
Non-negativity constraints
Numerical experiments
State-of-the-art techniques
True negative rates
Internet of things
TitlePrivacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems
TypeArticle
Pagination14271-14283
Volume Number9
dc.accessType Abstract Only


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