Cybersecurity for AI-Augmented Systems

Projektinformationen

Sec4AI4Sec

ID Finanzhilfevereinbarung: 101120393

DOI

10.3030/101120393

EK-Unterschriftsdatum 11 Juli 2023

Startdatum 1 Oktober 2023

Enddatum 30 September 2026

Finanziert unter

• Civil Security for Society

Gesamtkosten

€ 4 283 037,50

EU-Beitrag

€ 4 283 037,50

Koordiniert durch

UNIVERSITA DEGLI STUDI DI TRENTO
Italy

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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).

Veröffentlichungen

Peer reviewed articles (7)

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

Autoren: 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
Veröffentlicht in: IEEE Security & Privacy, 2024, ISSN 1558-4046
Herausgeber: IEEE
DOI: 10.1109/msec.2023.3343836

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

Autoren: Aurora Papotti, Ranindya Paramitha, Fabio Massacci
Veröffentlicht in: Empirical Software Engineering, Ausgabe 29, 2024, ISSN 1382-3256
Herausgeber: 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

Autoren: Marcello Meschini, Giorgio Di Tizio, Marco Balduzzi, Fabio Massacci
Veröffentlicht in: IEEE Transactions on Information Forensics and Security, Ausgabe 19, 2024, ISSN 1556-6013
Herausgeber: 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

Autoren: Fabio Massacci, Aurora Papotti, Ranindya Paramitha
Veröffentlicht in: Journal of Systems and Software, Ausgabe 211, 2024, ISSN 0164-1212
Herausgeber: Elsevier BV
DOI: 10.1016/j.jss.2024.111990

Technical leverage analysis in the Python ecosystem

Autoren: Ranindya Paramitha, Fabio Massacci
Veröffentlicht in: Empirical Software Engineering, Ausgabe 28, 2024, ISSN 1382-3256
Herausgeber: Springer Science and Business Media LLC
DOI: 10.1007/s10664-023-10355-2

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

Autoren: Raffaele Mura, Giuseppe Floris, Luca Scionis, Giorgio Piras, Maura Pintor, Ambra Demontis, Giorgio Giacinto, Battista Biggio, Fabio Roli
Veröffentlicht in: Neurocomputing, Ausgabe 616, 2024, ISSN 0925-2312
Herausgeber: Elsevier BV
DOI: 10.1016/j.neucom.2024.128918

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

Autoren: Quang-Cuong Bui, Ranindya Paramitha, Duc-Ly Vu, Fabio Massacci, Riccardo Scandariato
Veröffentlicht in: Empirical Software Engineering, Ausgabe 29, 2024, ISSN 1382-3256
Herausgeber: 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

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

A Rapid Review on Graph-Based Learning Vulnerability Detection

Autoren: Rosmaël Zidane Lekeufack Foulefack, Alessandro Marchetto
Veröffentlicht in: Communications in Computer and Information Science, Quality of Information and Communications Technology, 2024
Herausgeber: 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?

Autoren: Alessandro Marchetto
Veröffentlicht in: Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024), 2024
Herausgeber: ACM
DOI: 10.1145/3644032.3644448

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

Autoren: Simone Scalco, Ranindya Paramitha
Veröffentlicht in: Proceedings of the 21st International Conference on Mining Software Repositories, 2024
Herausgeber: ACM
DOI: 10.1145/3643991.3644871

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

Autoren: Simon Schneider, Ananya Saha, Emanuele Mezzi, Katja Tuma, Riccardo Scandariato
Veröffentlicht in: 2024 IEEE Secure Development Conference (SecDev), 2024
Herausgeber: IEEE
DOI: 10.1109/SecDev61143.2024.00007

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

Autoren: Andréa Doreste, Matteo Biagiola, Paolo Tonella
Veröffentlicht in: 2024 IEEE Conference on Software Testing, Verification and Validation (ICST), 2024
Herausgeber: IEEE
DOI: 10.1109/ICST60714.2024.00034