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45 results on '"Soman, K. P."'

2. An Empirical Comparative Study of Machine Learning Algorithms for Telugu News Classification

Author

Dhanush, S. V. S., Kumar, Tsaliki Satya Ganesh, Rohith, Dharavathu, Reddy, Penaka Vishnu, Soman, K. P., Kumar, S. Sachin, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kiran Mai, C., editor, Vasundhara, D. N., editor, Siva Kumar, P. V., editor, Srujan Raju, K., editor, Mukkamala, Raghava Rao, editor, Nagini, S., editor, Brahmananda Reddy, A., editor, Ramesh, G. S., editor, Baby, V., editor, and Venkateswara Rao, P., editor

Published
2023
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3. Tamil NLP Technologies: Challenges, State of the Art, Trends and Future Scope

Author

Rajendran, S., Anand Kumar, M., Rajalakshmi, Ratnavel, Dhanalakshmi, V., Balasubramanian, P., Soman, K P, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, M, Anand Kumar, editor, Chakravarthi, Bharathi Raja, editor, B, Bharathi, editor, O’Riordan, Colm, editor, Murthy, Hema, editor, Durairaj, Thenmozhi, editor, and Mandl, Thomas, editor

Published
2023
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4. Ensemble Deep Learning Models for Vehicle Classification in Motorized Traffic Analysis

Author

Asmitha, U., Roshan Tushar, S., Sowmya, V., Soman, K. P., 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, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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5. Hybrid Features-Based Ensembled Residual Convolutional Neural Network for Bird Acoustic Identification

Author

Theivaprakasham, Hari, Sowmya, V., Ravi, Vinayakumar, Gopalakrishnan, E. A., 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, Li, Yong, 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, Oneto, Luca, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Dhar, Sourav, editor, Do, Dinh-Thuan, editor, Sur, Samarendra Nath, editor, and Liu, Howard Chuan-Ming, editor

Published
2023
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8. Deep Learning-Based Topic Categorization of Tamil Social Media Text Content

Author

Geerthana Anusha, M., Sachin Kumar, S., 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, Li, Yong, 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, Oneto, Luca, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Gupta, Deepak, editor, Sambyo, Koj, editor, Prasad, Mukesh, editor, and Agarwal, Sonali, editor

Published
2022
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9. Deep Learning-Based Emotion Classification of Hindi Text from Social Media

Author

Kumar, Sruthi S., Kumar, S. Sachin, 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, Li, Yong, 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, Oneto, Luca, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Gupta, Deepak, editor, Sambyo, Koj, editor, Prasad, Mukesh, editor, and Agarwal, Sonali, editor

Published
2022
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10. Ensemble of Deep Transfer Learning Models for Parkinson's Disease Classification

Author

Rajanbabu, Kiranbabu, Veetil, Iswarya Kannoth, Sowmya, V., Gopalakrishnan, E. A., Soman, K. P., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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11. Modified UNet Architecture with Less Number of Learnable Parameters for Nuclei Segmentation

Author

Vishnu Priyal, S., Vyshnav, M. T., Sowmya, V., Soman, K. P., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2022
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13. Performance Improvement in Deep Learning Architecture for Phonocardiogram Signal Classification Using Spectrogram

Author

Kesav, R. Sai, Bhanu Prakash, M., Kumar, Krishanth, Sowmya, V., Soman, K. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Sonawane, V. R., editor

Published
2021
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14. Aspect-Based Sentiment Analysis in Hindi: Comparison of Machine/Deep Learning Algorithms

Author

Sai Aparna, T., Simran, K., Premjith, B., Soman, K. P., 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, Smys, S., editor, Balas, Valentina Emilia, editor, Kamel, Khaled A., editor, and Lafata, Pavel, editor

Published
2021
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16. 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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17. 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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18. 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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19. 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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20. 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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21. Facial Emotion Recognition Using Shallow CNN

Author

Sachin Saj, T. K., Babu, Seshu, Reddy, Vamsi Kiran, Gopika, P., Sowmya, V., Soman, K. P., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Trajkovic, Ljiljana, editor, Li, Kuan-Ching, editor, Das, Swagatam, editor, Wozniak, Michal, editor, and Berretti, Stefano, editor

Published
2020
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24. Remote Sensing Image Super-Resolution Using Residual Dense Network

Author

Jayanarayan, Abhijith, Sowmya, V., Soman, K. P., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2020
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25. Real-Time Speed Bump Detection Using Image Segmentation for Autonomous Vehicles

Author

Arunpriyan, J., Variyar, V. V. Sajith, Soman, K. P., Adarsh, S., 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, Pandian, A. Pasumpon, editor, Ntalianis, Klimis, editor, and Palanisamy, Ram, editor

Published
2020
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28. Time Split Based Pre-processing with a Data-Driven Approach for Malicious URL Detection

Author

Harikrishnan, N. B., Vinayakumar, R., Soman, K. P., Prabaharan Poornachandran, 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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29. 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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30. Improved DGA Domain Names Detection and Categorization Using Deep Learning Architectures with Classical Machine Learning Algorithms

Author

Vinayakumar, R., Soman, K. P., Prabaharan Poornachandran, Akarsh, S., Elhoseny, Mohamed, 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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31. Application of Deep Learning Architectures for Cyber Security

Author

Vinayakumar, R., Soman, K. P., Prabaharan Poornachandran, Akarsh, S., 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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32. Deep Learning Framework for Cyber Threat Situational Awareness Based on Email and URL Data Analysis

Author

Vinayakumar, R., Soman, K. P., Prabaharan Poornachandran, Akarsh, S., Elhoseny, Mohamed, 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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33. Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks

Author

Kumar, Amara Dinesh, Thodupunoori, Harish, Vinayakumar, R., Soman, K. P., Poornachandran, Prabaharan, Alazab, Mamoun, Venkatraman, Sitalakshmi, 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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34. 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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35. Anomaly Detection in Phonocardiogram Employing Deep Learning

Author

Sujadevi, V. G., Soman, K. P., Vinayakumar, R., Prem Sankar, A. U., 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, Behera, Himansu Sekhar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, and Abraham, Ajith, editor

Published
2019
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36. LSTM Based Paraphrase Identification Using Combined Word Embedding Features

Author

Aravinda Reddy, D., Anand Kumar, M., Soman, K. P., 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, Wang, Jiacun, editor, Reddy, G. Ram Mohana, editor, Prasad, V. Kamakshi, editor, and Reddy, V. Sivakumar, editor

Published
2019
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38. Deep Learning for Network Flow Analysis and Malware Classification

Author

Rahul, R. K., Anjali, T., Menon, Vijay Krishna, Soman, K. P., Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Thampi, Sabu M., editor, Martínez Pérez, Gregorio, editor, Westphall, Carlos Becker, editor, Hu, Jiankun, editor, Fan, Chun I., editor, and Gómez Mármol, Félix, editor

Published
2017
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40. 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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41. Retraction Note: Deep learning architectures for land cover classification using red and near-infrared satellite images.

Author

Unnikrishnan, Anju, Sowmya, V., and Soman, K. P.

Subjects
ZONING, REMOTE-sensing images, DEEP learning, PUBLISHING, CONFIDENCE
Abstract

The article titled "Deep learning architectures for land cover classification using red and near-infrared satellite images" has been retracted by the Editor-in-Chief and the publisher due to concerns identified during an investigation. These concerns include unsupported citations, non-standard phrasing, and image irregularities. The Editor-in-Chief no longer has confidence in the results and conclusions of the article. The authors Anju Unnikrishnan and V. Sowmya disagree with the retraction, while author K.P. Soman has not responded to correspondence regarding the retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]

Published
2024
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42. IDENTIFYING OIL PADS IN HIGH SPATIAL RESOLUTION AERIAL IMAGES USING FASTER R-CNN.

Author

Sunil, A., Sajithvariyar, V. V., Sowmya, V., Sivanpillai, R., and Soman, K. P.

Subjects
CONVOLUTIONAL neural networks, PETROLEUM, DEEP learning, ALGORITHMS, INDEPENDENT sets, ESSENTIAL oils
Abstract

Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-color aerial images. Faster R-CNN is a region-based object identification method, consisting of Regional Proposal Network (RPN) that helps to find the area where the target can be possibly present in the images. If the target is present in the images, the Faster R-CNN algorithm will identify the area in an image as foreground and the rest as background. The algorithm was trained with oil pad locations that were manually annotated from orthorectified imagery acquired in 2017. Eighty percent of the annotated images were used for training and the number of epochs was increased from 100 to 1000 in increments of 100 with a fixed length of 1000. After determining the optimal number of epochs the performance of the algorithm was evaluated with an independent set of validation images consisting of frames with and without oil pads. Results indicate that the Faster R-CNN algorithm can be used for identifying oil pads in aerial images. [ABSTRACT FROM AUTHOR]

Published
2021
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43. ANALYSIS OF FOUR GENERATOR ARCHITECTURES OF C-GAN, LOSS FUNCTION, AND ANNOTATION METHOD FOR EPIPHYTE IDENTIFICATION.

Author

Sajithvariyar, V. V., Aswin, S., Sowmya, V., Soman, K. P., Sivanpillai, R., and Brown, G. K.

Subjects
DEEP learning, GENERATIVE adversarial networks, ANNOTATIONS
Abstract

The deep learning (DL) models require timely updates to continue their reliability and robustness in prediction, classification, and segmentation tasks. When the deep learning models are tested with a limited test set, the model will not reveal the drawbacks. Every deep learning baseline model needs timely updates by incorporating more data, change in architecture, and hyper parameter tuning. This work focuses on updating the Conditional Generative Adversarial Network (C-GAN) based epiphyte identification deep learning model by incorporating 4 different generator architectures of GAN and two different loss functions. The four generator architectures used in this task are Resnet-6. Resnet-9, Resnet-50 and Resnet-101. A new annotation method called background removed annotation was tested to analyse the improvement in the epiphyte identification protocol. All the results obtained from the model by changing the above parameters are reported using two common evaluation metrics. Based on the parameter tuning experiment, Resnet-6, and Resnet- 9, with binary cross-entropy (BCE) as the loss function, attained higher scores also Resnet-6 with MSE as loss function performed well. The new annotation by removing the background had minimal effect on identifying the epiphytes. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Deep learning architectures for land cover classification using red and near-infrared satellite images.

Author

Unnikrishnan, Anju, Sowmya, V., and Soman, K. P.

Subjects
DEEP learning, ZONING, REMOTE-sensing images, NORMALIZED difference vegetation index, COLOR image processing, ARCHITECTURE, IMAGE processing
Abstract

Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification. [ABSTRACT FROM AUTHOR]

Published
2019
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45. Effect of Decolorized Images In Scene Classification Using Deep Convolution Features.

Author

Damodaran, Nikhil, Sowmya, V, Govind, D, and Soman, K P

Subjects
DEEP learning, ALGORITHMS, FEATURE extraction, ARTIFICIAL neural networks, GRAPHICS processing units, COMPUTER vision
Abstract

Abstract Scene classification is considered as an imperative issue for computer vision and has got extensive consideration in the recent past. Due to recent developments in high performance computing units such as GPUs, popularly known deep learning algorithm namely, convolutional neural networks (CNNs), exploits huge datasets to give powerful models. The paper proposes the use of transfer learning technique, by which a pre-trained model known as Places-CNN is used to generate feature vectors for each scene image of the dataset. The scene-classification experiments are conducted on the Oliva Torralba (OT) scene dataset, which consists of eight outdoor scene categories. The features were extracted from the fully connected layer of the pre-trained Places CNN architecture. The deep features were extracted from the input color images and the grayscale images converted using two different techniques based on singular value decomposition (SVD). The results obtained from classification experiments show that, models trained on SVD-Decolorized and Modified-SVD decolorized images give comparable performance to the input color images. Unlike the color images, which use three planes (RGB) of information, the grayscale images use only one plane of information. The grayscale images were able to retain the required shape and texture information from the original RGB images and, thus sufficient to categorize the classes of scene images. [ABSTRACT FROM AUTHOR]

Published
2018
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