Autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance
Author:
Affiliation:
1. Worcester Polytechnic Institute Worcester,Computer Science,United States of America
2. Mathematical Sciences, and Computer Science Worcester Polytechnic Institute Worcester,Data Science,United States of America
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10068862/10068777/10069397.pdf?arnumber=10069397
Reference24 articles.
1. Network intrusion detection system: A survey on artificial intelligence-based techniques;habeeb;Expert Systems,2022
2. Bonferroni correction;weisstein,2004
3. Network Anomaly Detection Using Transfer Learning Based on Auto-Encoders Loss Normalization
4. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
5. Nonlinear principal component analysis using autoassociative neural networks
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2. Exploring the Enhanced Performance of Unsupervised Feature Learning and Compression with Auto encoders;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29
3. Autoencoder Feature Residuals for Network Intrusion Detection: One-Class Pretraining for Improved Performance;Machine Learning and Knowledge Extraction;2023-07-31
4. One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection;2023 32nd International Conference on Computer Communications and Networks (ICCCN);2023-07
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