Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices
Author:
Affiliation:
1. Moroccan School of Engineering Sciences (EMSI),LSIA Laboratory,Tanger,Morocco
2. Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI),Abu Dhabi,UAE
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10592105/10592309/10592352.pdf?arnumber=10592352
Reference16 articles.
1. A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges
2. A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things
3. Deep Reinforcement Learning-Based Energy Efficiency Optimization for Flying LoRa Gateways
4. Privacy and Security for Resource-Constrained IoT Devices and Networks: Research Challenges and Opportunities
5. A Review of Security Standards and Frameworks for IoT-Based Smart Environments
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Efficient Intrusion Detection: Combining X2 Feature Selection with CNN-BiLSTM on the UNSW-NB15 Dataset;2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM);2024-07-23
2. Enhancing Intrusion Detection Systems Using Machine Learning Classifiers on the CSE-CIC-IDS2018 Dataset;2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM);2024-07-23
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