Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Sahabandu, Dinuka"'
Autor:
Niu, Luyao, Zhang, Hongchao, Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Clark, Andrew, Poovendran, Radha
Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous dec
Externí odkaz:
http://arxiv.org/abs/2410.20288
Autor:
Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Alexiou, Michail, Mertoguno, J. Sukarno, Bushnell, Linda, Poovendran, Radha
This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining
Externí odkaz:
http://arxiv.org/abs/2407.20466
Autor:
Li, Yuetai, Xu, Zhangchen, Jiang, Fengqing, Niu, Luyao, Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Poovendran, Radha
The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as chatbots and virtual assistants. However, the data used to train or f
Externí odkaz:
http://arxiv.org/abs/2406.12257
Autor:
Sahabandu, Dinuka, Xu, Xiaojun, Rajabi, Arezoo, Niu, Luyao, Ramasubramanian, Bhaskar, Li, Bo, Poovendran, Radha
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors. We show that such an adversary can ensure (1) high accuracy on both trigger-embedded and clean samples and (
Externí odkaz:
http://arxiv.org/abs/2402.08695
The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information. A DNN model may suffer from overfitting. Overfitted models have been shown to be susceptible to query-base
Externí odkaz:
http://arxiv.org/abs/2212.01688
Autor:
Sahabandu, Dinuka, Rajabi, Arezoo, Niu, Luyao, Li, Bo, Ramasubramanian, Bhaskar, Poovendran, Radha
Machine learning models in the wild have been shown to be vulnerable to Trojan attacks during training. Although many detection mechanisms have been proposed, strong adaptive attackers have been shown to be effective against them. In this paper, we a
Externí odkaz:
http://arxiv.org/abs/2207.05937
Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often necessitates converting the software
Externí odkaz:
http://arxiv.org/abs/2204.06624
Controlled islanding effectively mitigates cascading failures by partitioning the power system into a set of disjoint islands. In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adv
Externí odkaz:
http://arxiv.org/abs/2108.01628
Multi-agent Markov Decision Processes (MMDPs) arise in a variety of applications including target tracking, control of multi-robot swarms, and multiplayer games. A key challenge in MMDPs occurs when the state and action spaces grow exponentially in t
Externí odkaz:
http://arxiv.org/abs/2103.15894
Autor:
Moothedath, Shana, Sahabandu, Dinuka, Allen, Joey, Bushnell, Linda, Lee, Wenke, Poovendran, Radha
Publikováno v:
In Automatica January 2024 159
