WP3 – Vulnerabilities Management
Coordinated by
GRAD (M01-M30)
Objective
This work package has the following objectives:
- Analyze the state of the art to incorporate the latest advances in detection, forecasting and propagation of vulnerabilities
- Compile a representative dataset of software vulnerabilities, taking into account the data provided by BIECO’s use cases as well as from other public sources, and select the most representative features for an effective vulnerability detection process.
- Provide advanced tools to detect and forecast accurately vulnerabilities in ICT systems and components.
- Provide an advanced tool to analyze the propagation of vulnerabilities across the ICT supply chain.
Deliverables
- D3.1 Report on the state of the art of vulnerability management WP3 (5 – GRADIANT) Report Public M6
- D3.2 Dataset with software vulnerabilities WP3 (4 – UTC) Other Confidential, only for members of the consortium (including the Commission Services) M12
- D3.3 Report of the tools for vulnerability detection and forecasting WP3 (5 – GRADIANT) Report Public M18
- D3.4 Report of the tools for vulnerabilities propagation WP3 (5 – GRADIANT) Report Public M21
- D3.5 Updated Report of the tools for vulnerability detection and forecasting WP3 (5 – GRADIANT) Report Public M30
- D3.6 Updated Report of the tools for vulnerabilities propagation WP3 (5 – GRADIANT) Report Public M30
Outcomes
SafeML based reliability assessment
In earlier work, a statistical distance-based measure (SafeML) is proposed for machine learning components. In BIECO project, we propose extension of it with the use of Statistical Distance Dissimilarity across time series to obtain SDD based reliability and robustness estimate (StadRE and StadRO).
BIECO, un approccio olistico alla cybersecurity
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