SEIR: A Stackelberg game based approach for energy-aware and incentivized routing in selfish Opportunistic Networks. - Anshuman Chhabra; Vidushi Vashishth; Deepak Kumar Sharma (CISS, 2017)

A game theory based secure model against Black hole attacks in Opportunistic Networks. - Anshuman Chhabra; Vidushi Vashishth; Deepak Kumar Sharma (CISS, 2017)

An Elliptic Curve Cryptography Based Encryption Scheme for Securing the Cloud against Eavesdropping Attacks. - Anshuman Chhabra; Shivam Arora (CIC, 2017)

An Energy Efficient Routing Protocol for Wireless Internet-of-Things Sensor Networks. - Anshuman Chhabra; Vidushi Vashishth; Anirudh Khanna; Deepak Kumar Sharma; Jyotsna Singh (CoRR, 2018)

An approach to predictively securing critical cloud infrastructures through probabilistic modeling. - Satvik Jain; Arun Balaji Buduru; Anshuman Chhabra (CoRR, 2018)

A fuzzy logic and game theory based adaptive approach for securing opportunistic networks against black hole attacks. - Anshuman Chhabra; Vidushi Vashishth; Deepak Kumar Sharma (Int. J. Commun. Syst., 2018)

A moving target defense against adversarial machine learning. - Abhishek Roy 005; Anshuman Chhabra; Charles A. Kamhoua; Prasant Mohapatra (SEC, 2019)

A Machine Learning Approach Using Classifier Cascades for Optimal Routing in Opportunistic Internet of Things Networks. - Vidushi Vashishth; Anshuman Chhabra; Deepak Kumar Sharma (SECON, 2019)

GMMR: A Gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks. - Vidushi Vashishth; Anshuman Chhabra; Deepak Kumar Sharma (Comput. Commun., 2019)

Understanding flows in high-speed scientific networks: A Netflow data study. - Mariam Kiran; Anshuman Chhabra (Future Gener. Comput. Syst., 2019)

Suspicion-Free Adversarial Attacks on Clustering Algorithms. - Anshuman Chhabra; Abhishek Roy 005; Prasant Mohapatra (AAAI, 2020)

RLProph: a dynamic programming based reinforcement learning approach for optimal routing in opportunistic IoT networks. - Deepak Kumar Sharma; Joel J. P. C. Rodrigues; Vidushi Vashishth; Anirudh Khanna; Anshuman Chhabra (Wirel. Networks, 2020)

Fair Clustering Using Antidote Data. - Anshuman Chhabra; Adish Singla; Prasant Mohapatra (AFCR, 2021)

Moving Target Defense against Adversarial Machine Learning. - Anshuman Chhabra; Prasant Mohapatra (MTD@CCS, 2021)

An Overview of Fairness in Clustering. - Anshuman Chhabra; Karina Masalkovaite; Prasant Mohapatra (IEEE Access, 2021)

Fairness Degrading Adversarial Attacks Against Clustering Algorithms. - Anshuman Chhabra; Adish Singla; Prasant Mohapatra (CoRR, 2021)

Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions. - Mehul Sharma; Shrid Pant; Deepak Kumar Sharma; Koyel Datta Gupta; Vidushi Vashishth; Anshuman Chhabra (Trans. Emerg. Telecommun. Technol., 2021)

Fair Algorithms for Hierarchical Agglomerative Clustering. - Anshuman Chhabra; Prasant Mohapatra (ICMLA, 2022)

On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses. - Anshuman Chhabra; Ashwin Sekhari; Prasant Mohapatra (NeurIPS, 2022)

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations. - Muhammad Haroon; Anshuman Chhabra; Xin Liu 002; Prasant Mohapatra; Zubair Shafiq; Magdalena Wojcieszak (CoRR, 2022)

Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. - Anshuman Chhabra; Peizhao Li; Prasant Mohapatra; Hongfu Liu 001 (ICLR, 2023)

Towards Fair Video Summarization. - Anshuman Chhabra; Kartik Patwari; Chandana Kuntala; Sristi; Deepak Kumar Sharma; Prasant Mohapatra (Trans. Mach. Learn. Res., 2023)

Towards Robust and Fair Machine Learning - Anshuman Chhabra (2023)

"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection. - Anshuman Chhabra; Peizhao Li; Prasant Mohapatra; Hongfu Liu 001 (ICLR, 2024)

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias. - Anshuman Chhabra; Hadi Askari; Prasant Mohapatra (NAACL (Short Papers), 2024)

Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts. - Hadi Askari; Anshuman Chhabra; Bernhard Clemm von Hohenberg; Michael Heseltine; Magdalena Wojcieszak (CoRR, 2024)

Outlier Gradient Analysis: Efficiently Improving Deep Learning Model Performance via Hessian-Free Influence Functions. - Anshuman Chhabra; Bo Li; Jian Chen 016; Prasant Mohapatra; Hongfu Liu 001 (CoRR, 2024)

Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms. - Rajvardhan Oak; Muhammad Haroon; Claire Wonjeong Jo; Magdalena Wojcieszak; Anshuman Chhabra (ACL (1), 2025)

Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers. - Akshit Achara; Anshuman Chhabra (NAACL (Short Papers), 2025)

Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing. - Hadi Askari; Anshuman Chhabra; Muhao Chen 001; Prasant Mohapatra (NAACL (Findings), 2025)

Unraveling Indirect In-Context Learning Using Influence Functions. - Hadi Askari; Shivanshu Gupta; Terry Tong; Fei Wang 060; Anshuman Chhabra; Muhao Chen 001 (CoRR, 2025)

Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models. - Akash Bonagiri; Lucen Li; Rajvardhan Oak; Zeerak Babar; Magdalena Wojcieszak; Anshuman Chhabra (CoRR, 2025)

"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection. - Muhammad Haroon; Magdalena Wojcieszak; Anshuman Chhabra (CoRR, 2025)

LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions. - Hadi Askari; Shivanshu Gupta; Fei Wang 060; Anshuman Chhabra; Muhao Chen 001 (CoRR, 2025)

From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models. - Mahammed Kamruzzaman; Abdullah Al Monsur; Gene Louis Kim; Anshuman Chhabra (CoRR, 2025)