A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method

Popis výsledku v původním jazyce

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 (&lt;50 MB), and low detection time in a few seconds.

Název v anglickém jazyce

A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method

Popis výsledku anglicky

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 (&lt;50 MB), and low detection time in a few seconds.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

New Investigations in Artificial Life, AI, and Machine Learning

ISBN

978-1-79988-686-0

Počet stran výsledku

19

Strana od-do

141-159

Počet stran knihy

565

Název nakladatele

IGI Global

Místo vydání

Hershey, Pennsylvania

Kód UT WoS kapitoly

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