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Your search keyword '"Padmanabhan, Jayashree"' showing total 27 results
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27 results on '"Padmanabhan, Jayashree"'

1. Hybrid-AI Blockchain Supported Protection Framework for Smart Grids

Author

Sai Ganesh, S, Surya Siddharthan, S, Rajakumar, Balaji Rajaguru, Neelavathy Pari, S, Padmanabhan, Jayashree, Priya, Vishnu, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2022
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2. Effective Ensemble Dimensionality Reduction Approach Using Denoising Autoencoder for Intrusion Detection System

Author

Prabu, Saranya, Padmanabhan, Jayashree, Bala, Geetha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Raj, Jennifer S., editor, Palanisamy, Ram, editor, Perikos, Isidoros, editor, and Shi, Yong, editor

Published
2022
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6. Advanced Deep Neural Networks for Pattern Recognition: An Experimental Study

Author

Padmanabhan, Jayashree, Melvin Jose Premkumar, J., Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Abraham, Ajith, editor, Cherukuri, Aswani Kumar, editor, Madureira, Ana Maria, editor, and Muda, Azah Kamilah, editor

Published
2018
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9. Bi-channel hybrid GAN attention based anomaly detection system for multi-domain SDN environment.

Author

Prabu, Saranya and Padmanabhan, Jayashree

Subjects
*ANOMALY detection (Computer security), *SUPERVISED learning, *DEEP learning, *GENERATIVE adversarial networks, *SOFTWARE-defined networking
Abstract

Software-Defined Networking (SDN) is a strategy that leads the network via software by separating its control plane from the underlying forwarding plane. In support of a global digital network, multi-domain SDN architecture emerges as a viable solution. However, the complex and ever-evolving nature of network threats in a multi-domain environment presents a significant security challenge for controllers in detecting abnormalities. Moreover, multi-domain anomaly detection poses a daunting problem due to the need to process vast amounts of data from diverse domains. Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Traffic Engineering Based Attack Detection in Active Networks

Author

Padmanabhan, Jayashree, Easwarakumar, K. S., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Garg, Vijay, editor, Wattenhofer, Roger, editor, and Kothapalli, Kishore, editor

Published
2009
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27. Machine Learning in Automatic Speech Recognition: A Survey

Author

Padmanabhan, Jayashree and Johnson Premkumar, Melvin Jose

Abstract

ABSTRACTOver the past few decades, there has been tremendous development in machine learning paradigms used in automatic speech recognition (ASR) for home automation to space exploration. Though commercial speech recognizers are available for certain well-defined applications like dictation and transcription, many issues in ASR like recognition in noisy environments, multilingual recognition, and multi-modal recognition are yet to be addressed effectively. A comprehensive review of common machine learning techniques like artificial neural networks, support vector machines, and Gaussian mixture models along with hidden Markov models employed in ASR is provided. A thorough review on the recent developments in deep learning which has provided significant improvements in ASR performance, along with its relevance in the future of ASR, is also presented.

Published
2015
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