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6 results on '"Jui, Tonni Das"'

3. Contributors

Author

Ali, Sahara, primary, Alnuaim (Alnaim), Ahmed, additional, Ayoade, Olawale, additional, Barbedo, Jayme Garcia Arnal, additional, Beier, Colin M., additional, Bejarano, Gissella, additional, Bennett, Andrew, additional, Cervone, Guido, additional, Cristea, Nicoleta, additional, Didier, Annie, additional, Ganji, Geetha Satya Mounika, additional, Goh, Edwin, additional, Hu, Weiming, additional, Huang, Yiyi, additional, Islam, Didarul, additional, John, Aji, additional, Johnson, Lucas K., additional, Jui, Tonni Das, additional, Kale, Amruta, additional, Khanal, Bikram, additional, Lin, Wai Hang Chow, additional, Ma, Xiaogang, additional, Mahoney, Michael J., additional, Masrur, Arif, additional, Orduz, Javier, additional, Rafi, Nurul, additional, Rivas, Pablo, additional, Sooksatra, Korn, additional, Sun, Ziheng, additional, Tafur, Brenda, additional, Thompson, Christopher, additional, Wang, Jianwu, additional, Wang, Jinbo, additional, Yang, Kehan, additional, Young, George, additional, and Yu, Manzhu, additional

Published
2023
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4. AI ethics for earth sciences

Author

Rivas, Pablo, primary, Thompson, Christopher, additional, Tafur, Brenda, additional, Khanal, Bikram, additional, Ayoade, Olawale, additional, Jui, Tonni Das, additional, Sooksatra, Korn, additional, Orduz, Javier, additional, and Bejarano, Gissella, additional

Published
2023
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5. Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats.

Author

Rivas, Pablo, Orduz, Javier, Jui, Tonni Das, DeCusatis, Casimer, and Khanal, Bikram

Subjects
DENIAL of service attacks, CONVOLUTIONAL neural networks, QUANTUM computers, QUANTUM computing, QUANTUM mechanics, STOCHASTIC learning models
Abstract

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel "quanvolutional autoencoder" architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection. [ABSTRACT FROM AUTHOR]

Published
2024
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