Publications - BITS Pilani

Prof. Aneesh Sreevallabh Chivukula

Assistant Professor,
Dept. of Computer Science and Information Systems

Research Monographs, Technical Reports, Textbooks, and Handbooks

Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence

Aneesh Sreevallabh Chivukula, Xinghao Yang, Wei Liu, Bo Liu, and Wanlei Zhou, “Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence”. Springer Nature Switzerland.

Adversarial Deep Learning with Stackelberg Games

Aneesh Sreevallabh Chivukula, Xinghao Yang and Wei Liu, ``Adversarial Deep Learning with Stackelberg Games''. Book Chapter in Communications in Computer and Information Science, Springer International Publishing, 2019.

Big Data Analytics: Systems, Algorithms, Applications

C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, and L.M. Jenila Livingston, ``Big Data Analytics: Systems, Algorithms, Applications''. Springer Nature Singapore.

A Framework and Roadmap for Cloud Computing Innovation in India

Founding Member - Working Group on Big Data and Analytics : Cloud Computing Innovation Council of India by IEEE Standards Association, ``A Framework and Roadmap for Cloud Computing Innovation in India''. IEEE International Conference on Cloud Computing for Emerging Markets (CCEM 2013).

Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes

Micheal Lanham, ``Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes'' Berkeley, CA: Apress L. P, 2021 - Tech Review.

Conference Proceedings

A methodology for high availability of data for business continuity planning / disaster recovery in a grid using replication in a distributed database

Chidambaram, J.; Prabhu, C.; Narasimha Rao, P.A.; Wankar, R.; Aneesh, C.S.; Agarwal, A.; ,"A methodology for high availability of data for business continuity planning / disaster recovery in a grid using replication in a distributed database," Trends and Developments in Converging Technology(theme : innovative technologies for societal transformation), TENCON 2008 - IEEE Region 10 Conference, pp.1-6, 19-21 Nov. 2008, doi: 10.1109/TENCON.2008.4766862.

Incremental Novelty Detection applied to Complex Text Classification

Aneesh Sreevallabh Chivukula, Jean-Charles Lamirel, ``Incremental Novelty Detection applied to Complex Text Classification,'' {Proceedings of the 12th International Francophone Conference on Knowledge Extraction and Management} - EGC 2012, Presented at Atelier CIDN Classification Incrémentale et Détection de Nouveauté.

A new Feature Selection and Feature Contrasting approach based on Quality Metric: Application to Efficient Classification of Complex Textual Data

Jean-Charles Lamirel, Pascal Cuxac, Aneesh Sreevallabh Chivukula, Kafil Hajlaoui, ``A new Feature Selection and Feature Contrasting approach based on Quality Metric: Application to Efficient Classification of Complex Textual Data,'' Springer's Lecture Notes in Computer Science : Trends and Applications in Knowledge Discovery and Data Mining. Proceedings of 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining - PAKDD 2013, Presented at 3rd International Workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models.

Maximum Entropy based Associative Regression for Sparse Datasets

Aneesh Sreevallabh Chivukula, Vikram Pudi, ``Maximum Entropy based Associative Regression for Sparse Datasets,'' {Proceedings of the Web Intelligence Congress - WI 2014, Presented at Special Session on Complex Methods for Data and Web Mining.

A vectorized implementation for Maximum Entropy based Associative Regression

Aneesh Sreevallabh Chivukula, Vikram Pudi, ``A vectorized implementation for Maximum Entropy based Associative Regression,'' Proceedings of the International Conference on Soft Computing & Machine Intelligence - ISCMI 2014.

Adversarial learning games with deep learning models

Aneesh Sreevallabh Chivukula, Wei Liu, ``Adversarial learning games with deep learning models,'' Proceedings of the International Joint Conference on Neural Networks - IJCNN 2017.

Discovering Granger-Causal Features from Deep Learning Networks

Aneesh Sreevallabh Chivukula, Jun Li and Wei Liu, ``Discovering Granger-Causal Features from Deep Learning Networks,'' Proceedings of 31st Australasian Joint Conference in Artificial Intelligence - AI 2018.

Adversarial Deep Learning with Stackelberg Games

Aneesh Sreevallabh Chivukula, Xinghao Yang and Wei Liu, ``Adversarial Deep Learning with Stackelberg Games,'' Proceedings of the International Conference on Neural Information Processing - ICONIP 2019, Presented at the annual conference of the Asia-Pacific Neural Network Society.

Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach using Under-sampling and Class Weighting

Ayush Agarwal, Aneesh Sreevallabh Chivukula, Monowar H. Bhuyan, Tony Jan, Bhuva Narayan and Mukesh Prasad, ``Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach using Under-sampling and Class Weighting,'' Proceedings of the International Conference on Neural Information Processing - ICONIP 2020, Presented at the annual conference of the Asia-Pacific Neural Network Society.

Journal Articles

Off-Policy Actor-Critic Deep Reinforcement Learning methods for alert prioritization in Intrusion Detection Systems

Lalitha Chavali, Abhinav Krishnan, Paresh Saxena, Barsha Mitra, Aneesh Sreevallabh Chivukula, “Off-Policy Actor-Critic Deep Reinforcement Learning methods for alert prioritization in Intrusion Detection Systems,” Computers & Security - Elsevier COSE 2024, doi:10.1016/j.cose.2024.103854.

Game Theoretical Adversarial Deep Learning with Variational Adversaries

Aneesh Sreevallabh Chivukula, Xinghao Yang, Wei Liu, Tianqing Zhu, and Wanlei Zhou, ``Game Theoretical Adversarial Deep Learning with Variational Adversaries,'' IEEE Transactions on Knowledge and Data Engineering - TKDE 2020, doi:10.1109/TKDE.2020.2972320.

Adversarial Deep Learning Models with Multiple Adversaries

Aneesh Sreevallabh Chivukula, and Wei Liu, ``Adversarial Deep Learning Models with Multiple Adversaries,'' IEEE Transactions on Knowledge and Data Engineering - TKDE 2018, doi:10.1109/TKDE.2018.2851247.

Optimizing text classification through efficient feature selection based on quality metric

Jean-Charles Lamirel, Pascal Cuxac, Aneesh Sreevallabh Chivukula, and Kafil Hajlaoui, ``Optimizing text classification through efficient feature selection based on quality metric,'' Springer's Journal of Intelligent Information Systems. - EGC 2014, Presented at 14th International Francophone Conference on Knowledge Extraction and Management.

Eucalyptus Cloud to Remotely Provision e-Governance Applications

Sreerama Prabhu Chivukula, Rajasekhar Krovvidi, and Aneesh Sreevallabh Chivukula, “Eucalyptus Cloud to Remotely Provision e-Governance Applications,” Journal of Computer Networks and Communications - Hindawi Publishing Corporation, vol. 2011, Article ID 268987, 15 pages,2011.doi:10.1155/2011/268987. Presented at The Open Group India Conference 2011.

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Prof. Aneesh Sreevallabh Chivukula

Research Monographs, Technical Reports, Textbooks, and Handbooks

Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence

Adversarial Deep Learning with Stackelberg Games

Big Data Analytics: Systems, Algorithms, Applications

A Framework and Roadmap for Cloud Computing Innovation in India

Generating a New Reality: From Autoencoders and Adversarial Networks to Deepfakes

Conference Proceedings

A methodology for high availability of data for business continuity planning / disaster recovery in a grid using replication in a distributed database

Incremental Novelty Detection applied to Complex Text Classification

A new Feature Selection and Feature Contrasting approach based on Quality Metric: Application to Efficient Classification of Complex Textual Data

Maximum Entropy based Associative Regression for Sparse Datasets

A vectorized implementation for Maximum Entropy based Associative Regression

Adversarial learning games with deep learning models

Discovering Granger-Causal Features from Deep Learning Networks

Adversarial Deep Learning with Stackelberg Games

Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach using Under-sampling and Class Weighting

Journal Articles

Off-Policy Actor-Critic Deep Reinforcement Learning methods for alert prioritization in Intrusion Detection Systems

Game Theoretical Adversarial Deep Learning with Variational Adversaries

Adversarial Deep Learning Models with Multiple Adversaries

Optimizing text classification through efficient feature selection based on quality metric

Eucalyptus Cloud to Remotely Provision e-Governance Applications

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