Poster – IoTURVA: Securing Device-to-Device Communications for IoT

Ibbad Hafeez, Yi Ding, Markku Veli Johannes Antikainen, Sasu Arimo Olavi Tarkoma

Research output: Conference materialsPosterResearchpeer-review

Abstract

In this poster we present IoTurva, a platform for securing Device-to-Device (D2D) communication in IoT. Our solution takes a black-box approach to secure IoT edge-networks. We combine user and device-centric context-information together with network data to classify network communication as normal or malicious. We have designed a dual-layer traffic classification scheme based on fuzzy logic, where the classification model is trained remotely. The remotely trained model is then used by the edge gateway to classify the network traffic. We have implemented a proof-of-concept prototype and evaluate its performance in a real world environment. The evaluation shows that IoTurva causes very small overhead while it works with minimal hardware, and that our model training and classification approach can improve system efficiency and privacy.
Original languageEnglish
DOIs
Publication statusPublished - 18 Oct 2017

Fields of Science

  • 113 Computer and information sciences

Cite this

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title = "Poster – IoTURVA: Securing Device-to-Device Communications for IoT",
abstract = "In this poster we present IoTurva, a platform for securing Device-to-Device (D2D) communication in IoT. Our solution takes a black-box approach to secure IoT edge-networks. We combine user and device-centric context-information together with network data to classify network communication as normal or malicious. We have designed a dual-layer traffic classification scheme based on fuzzy logic, where the classification model is trained remotely. The remotely trained model is then used by the edge gateway to classify the network traffic. We have implemented a proof-of-concept prototype and evaluate its performance in a real world environment. The evaluation shows that IoTurva causes very small overhead while it works with minimal hardware, and that our model training and classification approach can improve system efficiency and privacy.",
keywords = "113 Computer and information sciences",
author = "Ibbad Hafeez and Yi Ding and Antikainen, {Markku Veli Johannes} and Tarkoma, {Sasu Arimo Olavi}",
note = "Volume: Proceeding volume:",
year = "2017",
month = "10",
day = "18",
doi = "10.1145/3117811.3131262",
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Poster – IoTURVA: Securing Device-to-Device Communications for IoT. / Hafeez, Ibbad; Ding, Yi; Antikainen, Markku Veli Johannes; Tarkoma, Sasu Arimo Olavi.

2017.

Research output: Conference materialsPosterResearchpeer-review

TY - CONF

T1 - Poster – IoTURVA: Securing Device-to-Device Communications for IoT

AU - Hafeez, Ibbad

AU - Ding, Yi

AU - Antikainen, Markku Veli Johannes

AU - Tarkoma, Sasu Arimo Olavi

N1 - Volume: Proceeding volume:

PY - 2017/10/18

Y1 - 2017/10/18

N2 - In this poster we present IoTurva, a platform for securing Device-to-Device (D2D) communication in IoT. Our solution takes a black-box approach to secure IoT edge-networks. We combine user and device-centric context-information together with network data to classify network communication as normal or malicious. We have designed a dual-layer traffic classification scheme based on fuzzy logic, where the classification model is trained remotely. The remotely trained model is then used by the edge gateway to classify the network traffic. We have implemented a proof-of-concept prototype and evaluate its performance in a real world environment. The evaluation shows that IoTurva causes very small overhead while it works with minimal hardware, and that our model training and classification approach can improve system efficiency and privacy.

AB - In this poster we present IoTurva, a platform for securing Device-to-Device (D2D) communication in IoT. Our solution takes a black-box approach to secure IoT edge-networks. We combine user and device-centric context-information together with network data to classify network communication as normal or malicious. We have designed a dual-layer traffic classification scheme based on fuzzy logic, where the classification model is trained remotely. The remotely trained model is then used by the edge gateway to classify the network traffic. We have implemented a proof-of-concept prototype and evaluate its performance in a real world environment. The evaluation shows that IoTurva causes very small overhead while it works with minimal hardware, and that our model training and classification approach can improve system efficiency and privacy.

KW - 113 Computer and information sciences

U2 - 10.1145/3117811.3131262

DO - 10.1145/3117811.3131262

M3 - Poster

ER -