Cybersecurity for AI-Augmented Systems

Informations projet

Sec4AI4Sec

N° de convention de subvention: 101120393

DOI

10.3030/101120393

Date de signature de la CE 11 Juillet 2023

Date de début 1 Octobre 2023

Date de fin 30 Septembre 2026

Financé au titre de

• Civil Security for Society

Coût total

€ 4 283 037,50

Contribution de l’UE

€ 4 283 037,50

Coordonné par

UNIVERSITA DEGLI STUDI DI TRENTO
Italy

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 (7)

Interim Open Science Results

Description of the components available as Open Science (dataset) (T2.2).

Communication and Dissemination Strategy

List of Sec4AI4Sec dissemination targets, channels and activities the project will use to reach them, and the related plan for each identified pathway (T2.1, T 2.2 and T2.3).

Sec4AI4Sec Integrated Toolflow Evaluation and Demonstration Plan – Report

Framework for the execution of the demonstrations to be run in T7.2 to T7.4, defining KPIs for the evaluation of the toolflow.

Interim Report on Communication and Dissemination

A report on general and pathway's dissemination and communication activities and results (T2.1, T2.2 and T2.3).

Initial Report on Evaluation of Trustworthy AI Models

Taxonomy of attacks against AI models (T4.1, T4.2) O3.R6, O3.R7

Intermediate Report on the Assurance Methodologies for Products and Services

Design and rules for security and gaps for AI/ML components (T6.1) O1.R1

ML-based Methods for Vulnerability Assessment - v1

Description of the projects approaches and tools for vulnerability assessment (T3.3) O5.R11

Data sets, microdata, etc (1)

Repository Mining Toolkit -v1

Design and first implementation of the repository mining toolkit (T3.1) O2.R3, P3.1

Websites, patent fillings, videos etc. (1)

Internet and Social Media Presences

The first release of the projects website (activities on T2.3).

Publications

Peer reviewed articles (7)

Known Vulnerabilities of Open Source Projects: Where Are the Fixes?

Auteurs: Antonino Sabetta; Serena Elisa Ponta; Rocio Cabrera Lozoya; Michele Bezzi; Tommaso Sacchetti; Matteo Greco; Gergő Balogh; Péter Hegedűs; Rudolf Ferenc; Ranindya Paramitha; Ivan Pashchenko; Aurora Papotti; Ákos Milánkovich; Fabio Massacci
Publié dans: IEEE Security & Privacy, 2024, ISSN 1558-4046
Éditeur: IEEE
DOI: 10.1109/msec.2023.3343836

On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools

Auteurs: Aurora Papotti, Ranindya Paramitha, Fabio Massacci
Publié dans: Empirical Software Engineering, Numéro 29, 2024, ISSN 1382-3256
Éditeur: Springer Science and Business Media LLC
DOI: 10.1007/s10664-024-10506-z

A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations

Auteurs: Marcello Meschini, Giorgio Di Tizio, Marco Balduzzi, Fabio Massacci
Publié dans: IEEE Transactions on Information Forensics and Security, Numéro 19, 2024, ISSN 1556-6013
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TIFS.2024.3456960

Addressing combinatorial experiments and scarcity of subjects by provably orthogonal and crossover experimental designs

Auteurs: Fabio Massacci, Aurora Papotti, Ranindya Paramitha
Publié dans: Journal of Systems and Software, Numéro 211, 2024, ISSN 0164-1212
Éditeur: Elsevier BV
DOI: 10.1016/j.jss.2024.111990

Technical leverage analysis in the Python ecosystem

Auteurs: Ranindya Paramitha, Fabio Massacci
Publié dans: Empirical Software Engineering, Numéro 28, 2024, ISSN 1382-3256
Éditeur: Springer Science and Business Media LLC
DOI: 10.1007/s10664-023-10355-2

HO-FMN: Hyperparameter optimization for fast minimum-norm attacks

Auteurs: Raffaele Mura, Giuseppe Floris, Luca Scionis, Giorgio Piras, Maura Pintor, Ambra Demontis, Giorgio Giacinto, Battista Biggio, Fabio Roli
Publié dans: Neurocomputing, Numéro 616, 2024, ISSN 0925-2312
Éditeur: Elsevier BV
DOI: 10.1016/j.neucom.2024.128918

APR4Vul: an empirical study of automatic program repair techniques on real-world Java vulnerabilities

Auteurs: Quang-Cuong Bui, Ranindya Paramitha, Duc-Ly Vu, Fabio Massacci, Riccardo Scandariato
Publié dans: Empirical Software Engineering, Numéro 29, 2024, ISSN 1382-3256
Éditeur: Springer Science and Business Media LLC
DOI: 10.1007/s10664-023-10415-7

Book chapters (2)

Towards the Use of Domain Knowledge to Enhance Transformer-Based Vulnerability Detection

Auteurs: Alessandro Marchetto, Rosmaël Zidane Lekeufack Foulefack
Publié dans: Communications in Computer and Information Science, Quality of Information and Communications Technology, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-70245-7_26

A Rapid Review on Graph-Based Learning Vulnerability Detection

Auteurs: Rosmaël Zidane Lekeufack Foulefack, Alessandro Marchetto
Publié dans: Communications in Computer and Information Science, Quality of Information and Communications Technology, 2024
Éditeur: Springer Nature Switzerland
DOI: 10.1007/978-3-031-70245-7_25

Conference proceedings (4)

Can explainability and deep-learning be used for localizing vulnerabilities in source code?

Auteurs: Alessandro Marchetto
Publié dans: Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024), 2024
Éditeur: ACM
DOI: 10.1145/3644032.3644448

Hash4Patch: A Lightweight Low False Positive Tool for Finding Vulnerability Patch Commits

Auteurs: Simone Scalco, Ranindya Paramitha
Publié dans: Proceedings of the 21st International Conference on Mining Software Repositories, 2024
Éditeur: ACM
DOI: 10.1145/3643991.3644871

Designing Secure AI-based Systems: a Multi-Vocal Literature Review

Auteurs: Simon Schneider, Ananya Saha, Emanuele Mezzi, Katja Tuma, Riccardo Scandariato
Publié dans: 2024 IEEE Secure Development Conference (SecDev), 2024
Éditeur: IEEE
DOI: 10.1109/SecDev61143.2024.00007

Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving

Auteurs: Andréa Doreste, Matteo Biagiola, Paolo Tonella
Publié dans: 2024 IEEE Conference on Software Testing, Verification and Validation (ICST), 2024
Éditeur: IEEE
DOI: 10.1109/ICST60714.2024.00034