Machine Learning Enabled Intrusion Detection for Edge Devices in the Internet of Things
Author:
Affiliation:
1. Data Science and Cybersecurity Center (DSC2), Howard University,Department of Electrical Engineering & Computer Science,Washington D.C.,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10099037/10098906/10099276.pdf?arnumber=10099276
Reference41 articles.
1. Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective
2. Adaptation of Machine Learning in Fog Computing: An Analytical Approach
3. An Efficient Intrusion Prevention System for CAN: Hindering Cyber-Attacks With a Low-Cost Platform
4. A Bayesian Rule Learning Based Intrusion Detection System for the MQTT Communication Protocol
5. A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis
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. Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends;Sensors;2024-03-20
2. SmartSentry: Cyber Threat Intelligence in Industrial IoT;IEEE Access;2024
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