Towards AI-enabled Cyber Threat Assessment in the Health Sector
- URL: http://arxiv.org/abs/2409.12765v1
- Date: Thu, 19 Sep 2024 13:34:34 GMT
- Title: Towards AI-enabled Cyber Threat Assessment in the Health Sector
- Authors: Patrizia Heinl, Andrius Patapovas, Michael Pilgermann,
- Abstract summary: The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization.
The platform delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cyber attacks on the healthcare industry can have tremendous consequences and the attack surface expands continuously. In order to handle the steadily rising workload, an expanding amount of analog processes in healthcare institutions is digitized. Despite regulations becoming stricter, not all existing infrastructure is sufficiently protected against cyber attacks. With an increasing number of devices and digital processes, the system and network landscape becomes more complex and harder to manage and therefore also more difficult to protect. The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization, analyzes it, delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures. Therefore, an architecture of such a platform is designed, relevant information sources are identified, and AI methods for relevant data collection, selection, and risk scoring are explored.
Related papers
- Countering Autonomous Cyber Threats [40.00865970939829]
Foundation Models present dual-use concerns broadly and within the cyber domain specifically.
Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations.
This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
arXiv Detail & Related papers (2024-10-23T22:46:44Z) - SoK: Security and Privacy Risks of Medical AI [14.592921477833848]
The integration of technology and healthcare has ushered in a new era where software systems, powered by artificial intelligence and machine learning, have become essential components of medical products and services.
This paper explores the security and privacy threats posed by AI/ML applications in healthcare.
arXiv Detail & Related papers (2024-09-11T16:59:58Z) - Securing The Future Of Healthcare: Building A Resilient Defense System For Patient Data Protection [0.0]
The study predicts the severity of healthcare data breaches using a gradientboosting machine learning model.
The findings revealed that hacking and IT incidents are the most common type of breaches in the healthcare industry.
The model evaluation showed that the gradient boosting algorithm performs well.
arXiv Detail & Related papers (2024-07-23T04:25:35Z) - Navigating the road to automotive cybersecurity compliance [39.79758414095764]
The automotive industry is compelled to adopt robust cybersecurity measures to safeguard both vehicles and data against potential threats.
The future of automotive cybersecurity lies in the continuous development of advanced protective measures and collaborative efforts among all stakeholders.
arXiv Detail & Related papers (2024-06-29T16:07:48Z) - Security in IS and social engineering -- an overview and state of the art [0.6345523830122166]
The digitization of all processes and the opening to IoT devices has fostered the emergence of a new formof crime, i.e. cybercrime.
The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, to the point of being perceived as the weak link'' of cybersecurity.
Knowing how to anticipate, identifying weak signals and outliers, detect early and react quickly to computer crime are therefore priority issues requiring a prevention and cooperation approach.
arXiv Detail & Related papers (2024-06-17T13:25:27Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - White paper on cybersecurity in the healthcare sector. The HEIR solution [1.3717071154980571]
Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns.
The HEIR project offers a comprehensive cybersecurity approach, promoting security features from various regulatory frameworks.
These measures aim to enhance digital health security and protect sensitive patient data while facilitating secure data access and privacy-aware techniques.
arXiv Detail & Related papers (2023-10-16T07:27:57Z) - A Systematization of Cybersecurity Regulations, Standards and Guidelines
for the Healthcare Sector [5.121113572240309]
This paper contributes a systematization of the significant cybersecurity documents relevant to the healthcare sector.
We collected the 49 most significant documents and used the NIST cybersecurity framework to categorize key information.
arXiv Detail & Related papers (2023-04-28T16:19:21Z) - Edge Intelligence for Empowering IoT-based Healthcare Systems [42.909808437026136]
This article highlights the benefits of edge intelligent technology, along with AI in smart healthcare systems.
A novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.
arXiv Detail & Related papers (2021-03-22T19:35:06Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918.
Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases.
We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
arXiv Detail & Related papers (2020-04-16T15:53:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.