BIECO

BIECO Integrated Platform will integrate the tools in a loosely coupled way.

Data Collection Tool (DCT) stores information from relevant vulnerability related datasets, providing a single access point to information required by the vulnerability detection and forecasting tools developed in T3.3, as well as for the failure prediction tools developed in T4.2.

Vulnerability Detection Tool will detect existing vulnerabilities within the source code which may lead to the successful execution of an attack.

Vulnerability Exploitability Forecasting Tool will estimate the probability of a vulnerability to be exploited in the next 3, 6 or 12 months.

Vulnerability Propagation Tool will calculate and offer the paths affected by a vulnerability in the source code.

Fuzzing Tool will test System Under Test (SUT) security vulnerabilities or inputs not contemplated that could compromise the system; as a black-box process, by using unintended or incorrect inputs and monitoring their corresponding outputs.

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.

Secured AI Investments platform using the outcome of the BIECO project.

Log Forgery Blocker – a brand new product on the market.

Security evaluation methodology to evaluate the security of an ICT system. The methodology is based on standards such as ISO 31000 standard for Risk Management, the ISO 29119 standard for Security Testing or the MUD standard…

The extended Manufacturer Usage Description (MUD) file is an extension of the MUD Internet Engineering Task Force (IETF) standard…

The monitoring tool is an infrastructure in charge of setting up and managing a monitoring component. It is based on event messages and enables the collection of complex events.

GdpR-based cOmbinatOrial Testing (GROOT) is a general combinatorial strategy for testing systems managing GDPR’s concepts (e.g., Data Subject, Personal Data or Controller).

The domain specific language enables specification of digital twin behaviour in a manner that can enable a predicted evaluation of its trustworthy behaviour in a simulated environment

Synchronization loss can occur due to many reasons, either because of a device or link failure or due to a targeted attack on the reference node, which supplies the corrected time to the network’s nodes.

Simulate the real time communication for the distributed based on the Time sensitive network simulation. Additionally, simulate the fail-operation clock synchronization methodology. This simulation will validate the communication stack.

Securing remote and in-vehicle communications against cyber-attacks, possibly performed with quantum computers in the near future, is a major goal in the automotive sector.

Once the secure communication with a vehicle has been demonstrated for remote FW update, it is straightforward to extend it to a general Service-Over-the-Air architecture and even further.

Ontology Manager is a Framework responsible for managing the Core Ontology used in BIECO, called DAEMON. It aims to support organizing concepts and their relationships related to System of Systems (SoS), Internet of Things (IoT), and System Components management and Monitoring.

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.

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.

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 (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.

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).