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shana moothedath
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A game-theoretic approach for dynamic information flow tracking to detect multistage advanced persistent threats
S Moothedath, D Sahabandu, J Allen, A Clark, L Bushnell, W Lee, ...
IEEE Transactions on Automatic Control 65 (12), 5248-5263, 2020
432020
A flow-network-based polynomial-time approximation algorithm for the minimum constrained input structural controllability problem
S Moothedath, P Chaporkar, MN Belur
IEEE Transactions on Automatic Control 63 (9), 3151-3158, 2018
252018
Minimum cost feedback selection for arbitrary pole placement in structured systems
S Moothedath, P Chaporkar, MN Belur
IEEE Transactions on Automatic Control 63 (11), 3881-3888, 2018
212018
Dynamic information flow tracking for detection of advanced persistent threats: A stochastic game approach
S Moothedath, D Sahabandu, J Allen, A Clark, L Bushnell, W Lee, ...
arXiv preprint arXiv:2006.12327, 2020
162020
A game theoretic approach for dynamic information flow tracking with conditional branching
D Sahabandu, S Moothedath, L Bushnell, R Poovendran, J Aller, W Lee, ...
2019 American Control Conference (ACC), 2289-2296, 2019
142019
Multi-stage dynamic information flow tracking game
S Moothedath, D Sahabandu, A Clark, S Lee, W Lee, R Poovendran
Decision and Game Theory for Security: 9th International Conference, GameSec …, 2018
132018
Approximating constrained minimum cost input–output selection for generic arbitrary pole placement in structured systems
S Moothedath, P Chaporkar, MN Belur
Automatica 107, 200-210, 2019
102019
Sparsest feedback selection for structurally cyclic systems with dedicated actuators and sensors in polynomial time
S Moothedath, P Chaporkar, MN Belur
IEEE Transactions on Automatic Control 64 (9), 3956-3963, 2019
82019
Optimal network topology design in composite systems for structural controllability
S Moothedath, P Chaporkar, MN Belur
IEEE Transactions on Control of Network Systems 7 (3), 1164-1175, 2020
72020
Minimizing inputs for strong structural controllability
K Yashashwi, S Moothedath, P Chaporkar
2019 American Control Conference (ACC), 2048-2053, 2019
72019
Stochastic conservative contextual linear bandits
J Lin, XY Lee, T Jubery, S Moothedath, S Sarkar, ...
2022 IEEE 61st Conference on Decision and Control (CDC), 7321-7326, 2022
62022
Dynamic information flow tracking games for simultaneous detection of multiple attackers
D Sahabandu, S Moothedath, J Allen, A Clark, L Bushnell, W Lee, ...
2019 IEEE 58th Conference on Decision and Control (CDC), 567-574, 2019
62019
Optimal selection of essential interconnections for structural controllability in heterogeneous subsystems
S Moothedath, P Chaporkar, MN Belur
Automatica 103, 424-434, 2019
62019
Stochastic dynamic information flow tracking game with reinforcement learning
D Sahabandu, S Moothedath, J Allen, L Bushnell, W Lee, R Poovendran
Decision and Game Theory for Security: 10th International Conference …, 2019
62019
Fully decentralized and federated low rank compressive sensing
S Moothedath, N Vaswani
2022 American Control Conference (ACC), 1491-1496, 2022
52022
Quickest detection of advanced persistent threats: A semi-markov game approach
D Sahabandu, J Allen, S Moothedath, L Bushnell, W Lee, R Poovendran
2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS …, 2020
52020
Target controllability of structured systems
S Moothedath, K Yashashwi, P Chaporkar, MN Belur
2019 18th European Control Conference (ECC), 3484-3489, 2019
52019
Distributed Stochastic Bandit Learning with Delayed Context Observation
J Lin, S Moothedath
2023 European Control Conference (ECC), 1-6, 2023
42023
A multi-agent reinforcement learning approach for dynamic information flow tracking games for advanced persistent threats
D Sahabandu, S Moothedath, J Allen, L Bushnell, W Lee, R Poovendran
arXiv preprint arXiv:2007.00076, 2020
42020
Learning equilibria in stochastic information flow tracking games with partial knowledge
S Misra, S Moothedath, H Hosseini, J Allen, L Bushnell, W Lee, ...
2019 IEEE 58th Conference on Decision and Control (CDC), 4053-4060, 2019
42019
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