HIGHLIGHTS
- who: Danish Attique and colleagues from the College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China have published the research work: Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries, in the Journal: Sensors 2022, 22, 9416. of /2022/
- what: The authors aim to train the proposed model on different datasets and enhance its detection strengths in the future.
- how: The same model is designed where a Convolutional Neural_Network (CNN) based threat detection scheme is developed.
- future: Calculating a confusion . . .
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