ResilBlockly
Coordinated by
RESILTECH
This exploitation result consists in cybersecurity consultancy services supported by ResilBlockly (former Blockly4SoS), a Model-Driven Engineering tool that has been developed in the context of BIECO.
Description
Describe the innovation content of the result:
A low complexity, still rigorous, solution for system-of-systems modelling supporting risk assessment and cybersecurity consultancies. The tool will enable the provision of these services at design time, during the early prototyping phase.
Who will be the customer?
Reference market is the critical infrastructures and systems, starting from the existing customers of Resiltech in transportation (e.g. automotive, railway, maritime). However, thanks to the modelling feature of the tool, which allows modelling at different levels of abstraction, and thanks to the implementation of a general risk assessment methodology, the value proposition is suitable to be used in several domains and the potential market embraces in general the SMEs interested in cybersecurity and risk assessment consultancies
What benefit will it bring to the customers?
Early identification of weaknesses and prompt performance of modifications on any existing infrastructures; identification of existing vulnerabilities to be eliminated or which likelihood of exploit needs to be significantly reduced.
When is the expected date of achievement in the project (Mth/yr)?
August 2021
When is the time to market (Mth/yr)?
08/2023
What are the costs to be incurred after the project and before exploitation?
Cost of tool maintenance: €20,000 (1 year), €35,000 (1-3 years)
• Cost of marketing initiatives and material: €3,000 (1 year), €7,500 (1-3 years)
• Cost of internal training and training of new resources: €2,000 (1 year), €5,000 (1-3 years)
• Personnel cost: €60,000 (1 year), €240,000 (1-3 years)
What is the approximate price range of this result/price of licences?
Price of consultancies to be determined (licensing is currently not in the plans)
What are the market size in Millions € for this result and relevant trend?
The security consulting services market reached $23.5 billion (approx. €20 billion) in 2020; source: Gartner Research)
How will this result rank against competing products in terms of price/performance?
To be determined
Who are the competitors for this result?
Competitors are companies and organizations in different domains trying to enter this market and offering risk assessment services, especially with the assistance of dedicated and proprietary software tools and following the approach of model-driven engineering and threat modelling
How fast and in what ways will the competition respond to this result?
Unknown at the moment
Who are the partners involved in the result?
Resiltech
Who are the industrial partners interested in the result (partners, sponsors, etc.)?
Unknown at the moment
Have you protected or will you protect this result? How? When?
IP rights for the source code of the tool are already reserved. The tool is currently available for the authorized partners of the project only.
Other results
Vulnerabilities Forecasting Tool
The Vulnerabilities Forecasting Tool (VFT) provides historical vulnerability data and projections for time intervals of 1, 2, 3, 6, and 12 months for several major software components.
Failure Prediction Tool
The Failure Prediction Tool (FPT) performs failure predictions by monitoring the logs of the applications that make up a system. It has a REST interface through which it receives in real time the log messages from the monitored applications.
safeTbox
The pre-existing tool safeTbox (www.safetbox.de) has been extended to support interoperation with the ResilBlockly tool for combined safety and security analysis.
Conditional Safety Certificates for ICT
Conditional Safety Certificates (ConSerts) have been applied to support resiliency of ICT infrastructures. Support for deployment and execution of ConSerts in ICT infrastructure according to use case needs was provided additionally.
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).