Abstract
IoT security detection plays an important role in securing the IoT ecosystem. The current detection systems suffer from poor fault tolerance and inefficient detection results. To address the IoT security vulnerability, the paper designs a multifeature fusion-based IoT security detection model to simulate an attacker sending test commands to IoT nodes. Firstly, the data collection algorithm is introduced, and the collected dataset is analyzed by three neural network models, namely, RNN, LSTM, and GRU, respectively. The best scoring model is selected as the classifier to identify vulnerabilities and achieve IoT security detection.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
