Search Results for author:
Found 10 papers, 0 papers with code
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses.
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way.
LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
In this work, we present LOCALINTEL, a novel automated knowledge contextualization system that, upon prompting, retrieves threat reports from the global threat repositories and uses its local knowledge database to contextualize them for a specific organization.
Use of Graph Neural Networks in Aiding Defensive Cyber Operations
In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information.
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security.
Impacts and Risk of Generative AI Technology on Cyber Defense
To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense.
TwinExplainer: Explaining Predictions of an Automotive Digital Twin
Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs.
A White-Box Adversarial Attack Against a Digital Twin
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs).
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
Explainable Artificial Intelligence (XAI)
Intrusion Detection
