GPU-based Classification for Wireless Intrusion Detection
Author:
Affiliation:
1. Youngstown State University, Youngstown, OH, USA
2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Funder
U.S. Department of Energy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3452411.3464445
Reference22 articles.
1. Feature Selection Improves Tree-based Classification for Wireless Intrusion Detection
2. A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
3. A Survey of Network Attacks on Cyber-Physical Systems
4. Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http://osc.edu/ark:/19495/f5s1ph73 Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http://osc.edu/ark:/19495/f5s1ph73
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3. Pick Quality Over Quantity: Expert Feature Selection and Data Preprocessing for 802.11 Intrusion Detection Systems;IEEE Access;2022
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