Log Forgery Blocker
Coordinated by
7bulls
Log Forgery Blocker – a brand new product on the market.
Description
Describe the innovation content of the result:
New product on the market – Log Forgery Blocker.
Who will be the customer?
The customers are companies which are using any ICT system, which need to achieve the highest level of non repudiability – like financial companies.
What benefit will it bring to the customers?
Log Forger Blocker will store hash codes of files using blockchain technology in a fully non repudiable way.
When is the expected date of achievement in the project (Mth/yr)?
At the end of the project.
When is the time to market (Mth/yr)?
At the end of the project.
What are the costs to be incurred after the project and before exploitation?
It is estimated at 10 – 20 kEUR.
What is the approximate price range of this result/price of licences?
Log Forgery Blocker will be open source, the revenue will be generated by professional services.
What are the market size in Millions € for this result and relevant trend?
Estimated market size – 10 mEUR yearly.
How will this result rank against competing products in terms of price/performance?
Log Forgery Blocker – there is no direct competition for the product.
Who are the competitors for this result?
Log Forgery Blocker – there is no direct competition for the product.
How fast and in what ways will the competition respond to this result?
We are estimating that creating similar solution will take at least one year.
Who are the partners involved in the result?
Log Forgery Blocker – no partnership foreseen.
Who are the industrial partners interested in the result (partners, sponsors, etc.)?
Financial companies which are using ICT systems and require non repudiability.
Have you protected or will you protect this result? How? When?
IP rights for source code are reserved.
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).
