Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks
Author:
Affiliation:
1. Brno University of Technology, Czech Republic
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3664476.3670440
Reference36 articles.
1. Binary and Multi-Class Classification on the IoT-23 Dataset
2. Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance
3. Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT Networks
4. RECON: A Real-time Entity Based Access Control for IoT
5. CICIDS2017 dataset: performance improvements and validation as a robust intrusion detection system testbed
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