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.
|Status||Publicerad - 18 okt. 2017|
- 113 Data- och informationsvetenskap