Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection-Reference-Cited by-同舟云学术

Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection

Published:2020-06-26 Issue: Volume: Page:
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Container-title:Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition
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Author:

Sinha Jay¹,Manollas M.¹

Affiliation:

1. Ramaiah Institute of Technology, Bangalore, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3430199.3430224

Reference30 articles.

1. [n.d.]. http://nsl.cs.unb.ca/NSL-KDD/ [n.d.]. http://nsl.cs.unb.ca/NSL-KDD/

2. [n.d.]. https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/ [n.d.]. https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/

3. Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection

4. Learning long-term dependencies with gradient descent is difficult

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