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. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are vital. To address this challenge and enable the generation of patient-centric responses that are contextually relevant and comprehensive, we propose MedInsight:a novel retrieval augmented framework that augments LLM inputs (prompts) with relevant background information from multiple sources. MedInsight extracts pertinent details from the patient's medical record or consultation transcript. It then integrates information from authoritative medical textbooks and curated web resources based on the patient's health history and condition. By constructing an augmented context combining the patient's record with relevant medical knowledge, MedInsight generates enriched, patient-specific responses tailored for healthcare applications such as diagnosis, treatment recommendations, or patient education. Experiments on the MTSamples dataset validate MedInsight's effectiveness in generating contextually appropriate medical responses. Quantitative evaluation using the Ragas metric and TruLens for answer similarity and answer correctness demonstrates the model's efficacy. Furthermore, human evaluation studies involving Subject Matter Expert (SMEs) confirm MedInsight's utility, with moderate inter-rater agreement on the relevance and correctness of the generated 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. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.

LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

Security Operations Center (SoC) analysts gather threat reports from openly accessible global threat databases and customize them manually to suit a particular organization's needs. These analysts also depend on internal repositories, which act as private local knowledge database for an organization. Credible cyber intelligence, critical operational details, and relevant organizational information are all stored in these local knowledge databases. Analysts undertake a labor intensive task utilizing these global and local knowledge databases to manually create organization's unique threat response and mitigation strategies. Recently, Large Language Models (LLMs) have shown the capability to efficiently process large diverse knowledge sources. We leverage this ability to process global and local knowledge databases to automate the generation of organization-specific threat intelligence. 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. LOCALINTEL comprises of three key phases: global threat intelligence retrieval, local knowledge retrieval, and contextualized completion generation. The former retrieves intelligence from global threat repositories, while the second retrieves pertinent knowledge from the local knowledge database. Finally, the fusion of these knowledge sources is orchestrated through a generator to produce a contextualized completion.

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. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes.

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI-Robotics systems has enhanced the quality our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that make up AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. 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. Our goal is to provide users, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.

Impacts and Risk of Generative AI Technology on Cyber Defense

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. 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. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.

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. In order to prevent these issues, it is crucial to continuously monitor the health of various subsystems and identify abnormal sensor channel behavior. Data-driven Digital Twin (DT) systems are capable of such a task. Current DT technologies utilize various Deep Learning (DL) techniques that are constrained by the lack of justification or explanation for their predictions. This inability of these opaque systems can influence decision-making and raises user trust concerns. This paper presents a solution to this issue, where the TwinExplainer system, with its three-layered architectural pipeline, explains the predictions of an automotive DT. Such a system can assist automotive stakeholders in understanding the global scale of the sensor channels and how they contribute towards generic DT predictions. TwinExplainer can also visualize explanations for both normal and abnormal local predictions computed by the DT.

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. The Digital Twin, which is typically described as consisting of a physical entity, a virtual counterpart, and the data connections in between, is increasingly being investigated as a means of improving the performance of physical entities by leveraging computational techniques, which are enabled by the virtual counterpart. This paper explores the susceptibility of Digital Twin (DT), a virtual model designed to accurately reflect a physical object using ML/DL classifiers that operate as Cyber Physical Systems (CPS), to adversarial attacks. As a proof of concept, we first formulate a DT of a vehicular system using a deep neural network architecture and then utilize it to launch an adversarial attack. We attack the DT model by perturbing the input to the trained model and show how easily the model can be broken with white-box attacks.

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). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.