Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning
- Submitting institution
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University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30246
- Type
- D - Journal article
- DOI
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10.1109/TIFS.2020.3042049
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 1720
- Volume
- 16
- Issue
- -
- ISSN
- 1556-6021
- Open access status
- Other exception
- Month of publication
- -
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduced the open-source tool MAGPIE for cyber-physical intrusion detection in smart homes. It is the first solution that is able to autonomously adjust the decision function of its underlying anomaly classification models to the smart home’s changing conditions. MAGPIE’s reasoning engine is now adapted in project H2020 ENSURESEC (GA 883242) to support the end-to-end cyber-physical security monitoring of e-commerce from purchase to physical delivery. It is also the basis of a new PhD project extending it with AI explainability capabilities.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
