TY  - CONF
ID  - SisLab4000
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4000/
A1  - Tran Viet, Khoa
A1  - Yuris Mulya, Saputra
A1  - Dinh Thai, Hoang
A1  - Nguyen Linh, Trung
A1  - Nguyen N, Diep
A1  - Nguyen Viet, Ha
A1  - Eryk, Dutkiewicz
Y1  - 2020/05//
N2  - Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be ef?ciently implemented in IoT Industry 4.0. In the system under consideration, we develop smart "filters" which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each ?lter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can signi?cantly reduce the information disclosure as well as network traf?c in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.
TI  - Collaborative learning model for cyberattack detection systems in IoT industry 4.0
M2  - Seoul, South Korea
AV  - none
T2  - IEEE Wireless Communications and Networking Conference (WCNC)
ER  -