DÏoT: A Crowdsourced Self-learning Approach for Detecting Compromised IoT Devices

By T. D. Nguyen, Samuel Marchal, Markus Miettinen, M. Dang, N. Asokan, Ahmad-Reza Sadeghi. 2018

56 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Wei Yang Bryan Lim
2. Dusist Niyato
3. Qiang Yang
4. Dinh Thai Hoang
5. Ying-Chang Liang
6. Sebastian Szyller
7. Nguyen Cong Luong
8. N. Asokan

Citations in the corpus

• PRADA: Protecting Against DNN Model Stealing Attacks (2018, background): “ision making. DNN models are being deployed in real-world systems for solving complex decision problems such as image and speech recognition [17], autonomous systems or automated detection of attacks [37]. New business models like Machine-Learningas-a-Service (MLaaS) have emerged where the model its”
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “The authors in [173] show empirically that by using FL, the system can successfully detect 95.”
• Peek-a-boo: i see your smart home activities, even encrypted! (2018, background): “Numerous works have shown that IoT devices can be identified with high accuracy for both WiFienabled [7, 15, 27, 28, 30] and BLE-enabled [16] devices.”