Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification

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dc.contributor.authorDucange, Pietro
dc.contributor.authorFazzolari, Michela
dc.contributor.authorMarcelloni, Francesco
dc.date.accessioned2023-12-26T18:37:38Z
dc.date.available2023-12-26T18:37:38Z
dc.date.issued2024-01-03
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.othere7665f9b-c923-459d-a49b-66ef8055ad08
dc.identifier.urihttps://hdl.handle.net/10125/106602
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoft Computing: Theory Innovations and Problem-Solving Benefits
dc.subjectexplainable artificial intelligence
dc.subjectinternet of things
dc.subjectintrusion detection systems
dc.subjecttrustworthy ai
dc.titleExplainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThe proliferation of IoT devices in our daily lives has raised concerns about the security of transmitted data. Due to their limited resources, IoT devices are vulnerable to malware attacks and cyber threats. Detecting and classifying these attacks is crucial to mitigate their impact. Various intrusion detection techniques have been proposed for IoT, including approaches based on ML and AI. Most of the ML/AI-based intrusion detection techniques, though effective, often lack transparency and trustworthiness. To address these aspects, XAI has emerged as a promising solution, providing insights on AI model decisions. In this work, we describe an explainable IDS in IoT networks which embeds a multi-way FDT as an XAI model for traffic classification. We propose a Cross-Device training and evaluation approach in which we evaluate the generalization capability of the IDS when new devices are connected to the IoT network without retraining the FDT.
dcterms.extent10 pages
prism.startingpage1784

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Soft Computing: Theory Innovations and Problem-Solving Benefits