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AHT-QCN: Adaptive Hunt Tuner Algorithm Optimized Q-learning Based Deep Convolutional Neural Network for the Penetration Testing Cover

AHT-QCN: Adaptive Hunt Tuner Algorithm Optimized Q-learning Based Deep Convolutional Neural Network for the Penetration Testing

Cybernetics and Information Technologies
Volume 24 (2024): Issue 3 (September 2024)
By: Dipali Railkar and  Shubhalaxmi Joshi  
Open Access
|Sep 2024

Authors

Dipali Railkar

railkar.dipali@gmail.com

School of Computer Science, Dr. Vishwanath Karad MIT World Peace University, Pune, India

Shubhalaxmi Joshi

shubhalaxmi.joshi@mitwpu.edu.in

School of Computer Science, Dr. Vishwanath Karad MIT World Peace University, Pune, India
DOI: https://doi.org/10.2478/cait-2024-0032 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
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Language: English
Page range: 182 - 196
Submitted on: Aug 5, 2024
Accepted on: Aug 21, 2024
Published on: Sep 19, 2024
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Deep learning,
Penetration testing,
Q-learning,
Adaptive hunt tuner algorithm,
Deep convolutional neural network
Related subjects:
Computer sciences,
Information technology

© 2024 Dipali Railkar, Shubhalaxmi Joshi, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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