activity
20162020
most citedMulti-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

22 citations · 22 across the 1 of their papers we have counts for

collaborators

6 papers

cs.GT2020

Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments

Sailik Sengupta, Kaustav Basu, Arunabha Sen +1

Electric power grid components, such as high voltage transformers (HVTs), generating stations, substations, etc. are expensive to maintain and, in the event of failure, replace. Th…

cs.GT202022 cited

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

Sailik Sengupta, Subbarao Kambhampati

The field of cybersecurity has mostly been a cat-and-mouse game with the discovery of new attacks leading the way. To take away an attacker's advantage of reconnaissance, researche…

cs.CR2019

A Survey of Moving Target Defenses for Network Security

Sailik Sengupta, Ankur Chowdhary, Abdulhakim Sabur +3

Network defenses based on traditional tools, techniques, and procedures fail to account for the attacker's inherent advantage present due to the static nature of network services a…

cs.AI2018

Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

Ankur Chowdhary, Sailik Sengupta, Dijiang Huang +1

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible securi…

cs.LG2018

Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases

Niharika Jain, Lydia Manikonda, Alberto Olmo Hernandez +2

The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners c…

cs.AI2016

Compliant Conditions for Polynomial Time Approximation of Operator Counts

Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta +2

In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains. The contribution of this abstract is threefold, we (1…