Detecting data manipulation attacks on physiological sensor measurements in wearable medical systems
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2510-523X
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Detecting data manipulation attacks on physiological sensor measurements in wearable medical systems ; volume:2018 ; number:1 ; day:29 ; month:9 ; year:2018 ; pages:1-21 ; date:12.2018
EURASIP journal on information security ; 2018, Heft 1 (29.9.2018), 1-21, 12.2018
- Creator
-
Cai, Hang
- Contributor
-
Venkatasubramanian, Krishna K.
SpringerLink (Online service)
- DOI
-
10.1186/s13635-018-0082-y
- URN
-
urn:nbn:de:101:1-2018110712214088038760
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:44 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cai, Hang
- Venkatasubramanian, Krishna K.
- SpringerLink (Online service)
Other Objects (12)
Detecting Inference Attacks Involving Sensor Data
Detecting Sybil attacks in vehicular networks
Transfer learning for detecting unknown network attacks
Detecting IMSI-catchers and other mobile network attacks
Adversarials−1: detecting adversarial inputs with internal attacks
Detecting Software Attacks on Embedded IoT Devices
Detecting peripheral-based attacks on the host memory
Counteracting phishing through HCI : detecting attacks and warning users
Detecting web attacks with end-to-end deep learning
Counteracting phishing through HCI : detecting attacks and warning users
Detecting cybersecurity attacks across different network features and learners
Detecting Denial of Service attacks using machine learning algorithms
Detecting Inference Attacks Involving Sensor Data
Detecting Sybil attacks in vehicular networks
Transfer learning for detecting unknown network attacks
Detecting IMSI-catchers and other mobile network attacks
Adversarials−1: detecting adversarial inputs with internal attacks
Detecting Software Attacks on Embedded IoT Devices
Detecting peripheral-based attacks on the host memory
Counteracting phishing through HCI : detecting attacks and warning users
Detecting web attacks with end-to-end deep learning
Counteracting phishing through HCI : detecting attacks and warning users
Detecting cybersecurity attacks across different network features and learners
Detecting Denial of Service attacks using machine learning algorithms
Detecting Inference Attacks Involving Sensor Data
Detecting Sybil attacks in vehicular networks
Transfer learning for detecting unknown network attacks
Detecting IMSI-catchers and other mobile network attacks
Adversarials−1: detecting adversarial inputs with internal attacks
Detecting Software Attacks on Embedded IoT Devices
Detecting peripheral-based attacks on the host memory
Counteracting phishing through HCI : detecting attacks and warning users
Detecting web attacks with end-to-end deep learning
Counteracting phishing through HCI : detecting attacks and warning users
Detecting cybersecurity attacks across different network features and learners