AI Attack and Defense for the Smart Healthcare

Informations projet

ANTIDOTE

N° de convention de subvention: 101183162

DOI

10.3030/101183162

Date de signature de la CE 21 Novembre 2024

Date de début 1 Janvier 2025

Date de fin 31 Decembre 2028

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 1 600 800,00

Coordonné par

UNIVERSITY OF PIRAEUS RESEARCH CENTER
Greece

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (2)

Attack exploration and Adversarial AI Threat model for Smart Healthcare

This deliverable will identify attack examples of AI models applied in cybersecurity and design the adversarial AI threat model that will also include possible countermeasures against the identified threats.

ANTIDOTE Requirements & Reference Architecture

This deliverable will provide the specification of the ANTIDOTE reference architecture, the functional requirements provide an analysis and point of reference for the ANTIDOTE in relation to the two specific use cases.

Data Management Plan (1)

Quality Assurance and Data Management Plan

It will describe the project quality plan and the data management plan, and gender equality plan.

Publications

Peer reviewed articles (5)

Testing the limits: exploring adversarial techniques in AI models

Auteurs: Apostolis Zarras, Athanasia Kollarou, Aristeidis Farao, Panagiotis Bountakas, Christos Xenakis
Publié dans: PeerJ Computer Science, Numéro 11, 2025, ISSN 2376-5992
Éditeur: PeerJ
DOI: 10.7717/PEERJ-CS.3330

A comprehensive survey of cybersecurity techniques based on quality of service (QoS) on the Internet of Things (IoT)

Auteurs: Sefati, Seyed Salar; Arasteh, Bahman; Simona Viorica, HALUNGA; Fratu, Octavian
Publié dans: Cluster Computing, 2025
Éditeur: SPRINGER NATURE
DOI: 10.1007/S10586-025-05449-Z

A Bedbug Optimization-Based Machine Learning Framework for Software Fault Prediction

Auteurs: Arasteh, Bahman; Sefati, Seyed Salar; POPOVICI, Eduard Cristian; Ince, Ibrahim Furkan; Kiani, Farzad
Publié dans: Mathematics, 2025
Éditeur: MDPI
DOI: 10.3390/MATH13213531

Unleashing the Power of Wireless Communication in Healthcare by Empowering Patient Care and Connectivity: A Comprehensive Survey

Auteurs: Urvashi Chaudhary, Mohammad Furqan Ali, Ambrish Kumar, Abhishek Sharma, Dushantha K. Nalin Jayakody
Publié dans: IEEE Access, Numéro 13, 2025, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2025.3578344

SSLA: a semi-supervised framework for real-time injection detection and anomaly monitoring in cloud-based web applications with real-world implementation and evaluation

Auteurs: Seyed Salar Sefati, Bahman Arasteh, Octavian Fratu, Simona Halunga
Publié dans: Journal of Cloud Computing, Numéro 14, 2025, ISSN 2192-113X
Éditeur: Springer Science and Business Media LLC
DOI: 10.1186/S13677-025-00765-6

Conference proceedings (1)

Red vs. Blue Team Training Scenarios for 5G/6G Networks

Auteurs: Stylianos Karagiannis, Antonios Kasotakis, Emmanouil Magkos, Christoforos Ntantogian
Publié dans: Lecture Notes in Computer Science, Availability, Reliability and Security, 2025
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-032-00642-4_3