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Your search keyword '"Soman, K. P."' showing total 10 results
Start Over Author "Soman, K. P." Topic cyber security
10 results on '"Soman, K. P."'

1. DCNN-IDS: Deep Convolutional Neural Network Based Intrusion Detection System

Author

Sriram, S., Shashank, A., Vinayakumar, R., Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balusamy, Suresh, editor, Dudin, Alexander N., editor, Graña, Manuel, editor, Mohideen, A. Kaja, editor, Sreelaja, N. K., editor, and Malar, B., editor

Published
2020
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2. Deep Learning Based Frameworks for Handling Imbalance in DGA, Email, and URL Data Analysis

Author

Simran, K., Balakrishna, Prathiksha, Vinayakumar, Ravi, Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balusamy, Suresh, editor, Dudin, Alexander N., editor, Graña, Manuel, editor, Mohideen, A. Kaja, editor, Sreelaja, N. K., editor, and Malar, B., editor

Published
2020
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3. Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security

Author

Simran, K., Sriram, S., Vinayakumar, R., Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Hegde, Rajesh M., editor, Krishnan, Sri, editor, Mukhopadhyay, Jayanta, editor, Chaudhary, Vipin, editor, Marques, Oge, editor, Piramuthu, Selwyn, editor, and Corchado, Juan M., editor

Published
2020
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4. Towards Evaluating the Robustness of Deep Intrusion Detection Models in Adversarial Environment

Author

Sriram, S., Simran, K., Vinayakumar, R., Akarsh, S., Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Martinez Perez, Gregorio, editor, Ko, Ryan, editor, and Rawat, Danda B., editor

Published
2020
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5. Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream

Author

Simran, K., Balakrishna, Prathiksha, Vinayakumar, R., Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Martinez Perez, Gregorio, editor, Ko, Ryan, editor, and Rawat, Danda B., editor

Published
2020
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6. Cost-Sensitive Long Short-Term Memory for Imbalanced DGA Family Categorization

Author

Mohammed Harun Babu, R., Vinayakumar, R., Soman, K. P., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Sengodan, Thangaprakash, editor, Murugappan, M., editor, and Misra, Sanjay, editor

Published
2020
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8. A Detailed Investigation and Analysis of Deep Learning Architectures and Visualization Techniques for Malware Family Identification

Author

Akarsh, S., Prabaharan Poornachandran, Menon, Vijay Krishna, Soman, K. P., Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Hassanien, Aboul Ella, editor, and Elhoseny, Mohamed, editor

Published
2019
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9. DBD: Deep Learning DGA-Based Botnet Detection

Author

Vinayakumar, R., Soman, K. P., Poornachandran, Prabaharan, Alazab, Mamoun, Jolfaei, Alireza, Masys, Anthony J., Series Editor, Bichler, Gisela, Advisory Editor, Bourlai, Thirimachos, Advisory Editor, Johnson, Chris, Advisory Editor, Karampelas, Panagiotis, Advisory Editor, Leuprecht, Christian, Advisory Editor, Morse, Edward C., Advisory Editor, Skillicorn, David, Advisory Editor, Yamagata, Yoshiki, Advisory Editor, Alazab, Mamoun, editor, and Tang, MingJian, editor

Published
2019
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10. Adversarial Defense: DGA-Based Botnets and DNS Homographs Detection Through Integrated Deep Learning.

Author

Ravi, Vinayakumar, Alazab, Mamoun, Srinivasan, Sriram, Arunachalam, Ajay, and Soman, K. P.

Subjects
INTERNET domain naming system, DEEP learning, BOTNETS, REVERSE engineering, HOMONYMS, CYBERTERRORISM, HUMAN error
Abstract

Cybercriminals use domain generation algorithms (DGAs) to prevent their servers from being potentially blacklisted or shut down. Existing reverse engineering techniques for DGA detection is labor intensive, extremely time-consuming, prone to human errors, and have significant limitations. Hence, an automated real-time technique with a high detection rate is warranted in such applications. In this article, we present a novel technique to detect randomly generated domain names and domain name system (DNS) homograph attacks without the need for any reverse engineering or using nonexistent domain (NXDomain) inspection using deep learning. We provide an extensive evaluation of our model over four large, real-world, publicly available datasets. We further investigate the robustness of our model against three different adversarial attacks: DeepDGA, CharBot, and MaskDGA. Our evaluation demonstrates that our method is effectively able to identify DNS homograph attacks and DGAs and also is resilient to common evading cyberattacks. Promising results show that our approach provides a more effective detection rate with an accuracy of 0.99. Additionally, the performance of our model is compared against the most popular deep learning architectures. Our findings highlight the essential need for more robust detection models to counter adversarial learning. [ABSTRACT FROM AUTHOR]

Published
2023
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