A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50018942" target="_blank" >RIV/62690094:18450/22:50018942 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.4018/978-1-7998-8686-0.ch007" target="_blank" >http://dx.doi.org/10.4018/978-1-7998-8686-0.ch007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4018/978-1-7998-8686-0.ch007" target="_blank" >10.4018/978-1-7998-8686-0.ch007</a>
Alternative languages
Result language
angličtina
Original language name
A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method
Original language description
Smart telemetry medical devices do not have sufficient security measures, making them weak against different attacks. Machine learning (ML) has been broadly used for cyber-attack detection via on-gadgets and on-chip embedded models, which need to be held along with the medical devices, but with limited ability to perform heavy computations. The authors propose a real-time and lightweight fog computing-based threat detection using telemetry sensors data and their network traffic in NetFlow. The proposed method saves memory to a great extent as it does not require retraining. It is based on an incremental form of Hoeffding Tree Naïve Bayes Adaptive (HTNBA) and Incremental K-Nearest Neighbors (IKNN) algorithm. Furthermore, it matches the nature of sensor data which increases in seconds. Experimental results showed that the proposed model could detect different attacks against medical sensors with high accuracy (»100%), small memory usage (<50 MB), and low detection time in a few seconds.
Czech name
—
Czech description
—
Classification
Type
C - Chapter in a specialist book
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
New Investigations in Artificial Life, AI, and Machine Learning
ISBN
978-1-79988-686-0
Number of pages of the result
19
Pages from-to
141-159
Number of pages of the book
565
Publisher name
IGI Global
Place of publication
Hershey, Pennsylvania
UT code for WoS chapter
—