IoT Components LifeCycle Based Security Analysis
Title | IoT Components LifeCycle Based Security Analysis |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Marconot, J., Pebay-Peyroula, F., Hély, D. |
Conference Name | 2017 Euromicro Conference on Digital System Design (DSD) |
Date Published | 28 September 201 |
Publisher | IEEE |
ISBN Number | 978-1-5386-2146-2 |
Keywords | biomedical electronics, biomedical equipment, complex interactions, composability, Computer architecture, connected insulin pump, EBIOS methodology, electronic device lifecycle, Embedded systems, generic model, Hardware, Insulin pumps, Internet of Things, IoT component lifecycle, lifecycle induced vulnerabilities, Metrics, performance evaluation, Production, pubcrawl, Resiliency, security, security analysis, security of data, Software |
Abstract | We present in this paper a security analysis of electronic devices which considers the lifecycle properties of embedded systems. We first define a generic model of electronic devices lifecycle showing the complex interactions between the numerous assets and the actors. The method is illustrated through a case study: a connected insulin pump. The lifecycle induced vulnerabilities are analyzed using the EBIOS methodology. An analysis of associated countermeasures points out the lack of consideration of the life cycle in order to provide an acceptable security level of each assets of the device. |
URL | http://ieeexplore.ieee.org/document/8049800/?reload=true |
DOI | 10.1109/DSD.2017.44 |
Citation Key | marconot_iot_2017 |
- Insulin pumps
- biomedical electronics
- biomedical equipment
- complex interactions
- composability
- computer architecture
- connected insulin pump
- EBIOS methodology
- electronic device lifecycle
- embedded systems
- generic model
- Hardware
- Software
- Internet of Things
- IoT component lifecycle
- lifecycle induced vulnerabilities
- Metrics
- performance evaluation
- Production
- pubcrawl
- Resiliency
- security
- Security analysis
- security of data