Cyber vulnerability ranking prediction by prescription
BANDO: PR Puglia FESR FSE+ 2021-2027 (fondo FESR) Azione 1.5 Interventi per il rafforzamento del sistema innovativo regionale e sostegno alla collaborazione tra imprese e strutture di ricerca” – Avviso “RETI – Sostegno alla ricerca collaborativa”
The CYBER-PREDICT project aims to develop a SaaS (Software as a Service) solution for the automated assessment of cybersecurity vulnerabilities through Machine Learning algorithms. The objectives are:
- Design and develop a web-based platform featuring an intuitive user interface for the structured input and analysis of vulnerability descriptions.
- Implement a Front-End architecture focused on usability and configurable levels of depth for CVSS evaluation.
- Implement a Back-End architecture to manage communication with the Machine Learning model, process vulnerability descriptions, and return the predicted CVSS Score.
- Develop a Machine Learning model for the automated analysis of textual vulnerability data and accurate prediction of the corresponding CVSS Score.
Consortium:
- Eulogic NT S.p.A.
- Resiltech S.r.l.
- Università degli Studi di Bari – Dipartimento di Informatica
- Università del Salento -Dipartimento Ingegneria dell’Innovazione
In the CYBER-PREDICT project, Resiltech is in charge of:
- Defining a Secure Development LifeCycle (SDL) integrated into OT development processes.
- Analyzing model-driven engineering methodologies for early-stage vulnerability identification.
- Designing a Threat Meta-Model supporting asset identification, severity assessment, and management of security-related issues across the life cycle.
- Evaluating the adoption of Defence-in-Depth strategies to enhance system resilience through layered security controls and technical guidelines.