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Prashant Rawat

Publications and source records attributed to Prashant Rawat.

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An Emulation Anchored Digital Twin Testbed for Cyberattack and Defense Analysis in Hospital IT OT Environments

Modern hospitals increasingly rely on integrated Information Technology (IT) and Operational Technology (OT) infrastructures to support critical healthcare services. However, this convergence expands the cybersecurity attack surface and makes safe validation of defensive mechanisms difficult on live systems. Existing testbeds often focus on isolated IT or OT environments and do not capture realistic cross-domain healthcare interactions. This work presents a hospital IT and OT cybersecurity testbed coupled with a digital twin for monitoring, experimentation, and validation of countermeasures. The testbed emulates a central server, Electronic Health Record (EHR) systems, SCADA-based infrastructure, and segmented IT, OT, and DMZ networks. It supports controlled cyberattack execution, software-patch evaluation, and training of RL-based defense agents. The testbed is further extended to a digital twin that models the real-time state of the environment using log and network statistics and enables bidirectional interaction through command execution and container lifecycle orchestration. Modbus/TCP and FHIR/HL7 support realistic communication across healthcare and industrial components. Experimental evaluation shows low computation overhead, with average normalized CPU utilization below 0.4 % per container and most lightweight services operating below 0.01%. OpenPLC Modbus TCP operations achieve a median round-trip latency of 0.901 ms. The testbed also captures a multi-stage SSH-based attack propagating from the DMZ to the IT and PLC networks. The framework provides a foundation for extending the emulated environment toward a hardware-enabled hospital digital twin.

cs.CR

Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition

Recent studies have demonstrated that prompting large language models (LLM) with audio encodings enables effective speech recognition capabilities. However, the ability of Speech LLMs to comprehend and process multi-channel audio with spatial cues remains a relatively uninvestigated area of research. In this work, we present directional-SpeechLlama, a novel approach that leverages the microphone array of smart glasses to achieve directional speech recognition, source localization, and bystander cross-talk suppression. To enhance the model's ability to understand directivity, we propose two key techniques: serialized directional output training (S-DOT) and contrastive direction data augmentation (CDDA). Experimental results show that our proposed directional-SpeechLlama effectively captures the relationship between textual cues and spatial audio, yielding strong performance in both speech recognition and source localization tasks.

eess.AS