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THREAT-ARREST Cyber Security Threats and Threat Actors Training - Assurance Driven Multi-Layer, end-to-end Simulation and Training

Project description

Building cyber resilience through an adaptive security training platform

In today’s digital age, so many organisations rely on information technology, making them vulnerable to cyberattacks. Maintaining effective cybersecurity can be challenging and costly when technologies are constantly advancing in both security solutions and threats. The use of new intelligent cybersecurity tools requires knowledgeable and experienced specialists. In response, the EU-funded THREAT-ARREST project aims to develop an advanced training platform for key security personnel within organisations. Trainees will be guided through training scenarios that cater to their level of expertise, followed by a performance evaluation. The prototype’s efficiency will be assessed and validated in pilot smart energy, healthcare and shipping areas.

Objective

The challenge of mitigating advanced cyber attacks through advanced security training has been evident during the last decade in numerous fields and industries in EU. Despite the wide spectrum of such tools appears to provide a comprehensive machinery for detecting and responding effectively to cyber attacks, it is difficult to establish effective tool usage strategies and processes for addressing the ever-expanding land-scape of these attacks. Moreover, the advent of more “intelligent” cybersecurity solutions, which make use of technologies like machine learning, statistical analysis and user behaviour analysis, requires sophisticated and hands-on training of the key personnel of organizations, who have responsibility for security, for the latter to be able to master them. In response to the above, THREAT-ARREST will develop an advanced training platform incorporating emulation, simulation, serious gaming and visualization capabilities to adequately prepare stakeholders with different types of responsibility and levels of expertise in defending high-risk cyber systems and organizations to counter advanced, known and new cyber attacks. The THREAT-ARREST platform will deliver security training, based on a model driven approach where cyber threat and training preparation models, specifying the potential attacks, the security controls of cyber systems against them, and the tools that may be used to assess the effectiveness of these controls, will drive the training process, and align it with operational cyber system security assurance mechanisms to ensure the relevance of training. The platform will also support trainee performance evaluation and training program evaluation and adapt training programs based on them. The effectiveness of the framework will be validated using a prototype implementation at TRL-7 inter-connected with real cyber systems pilots in the area of smart energy, healthcare and shipping, and from technical, legal and business perspectives.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

IA - Innovation action

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Call for proposal

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(opens in new window) H2020-DS-2016-2017

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Coordinator

IDRYMA TECHNOLOGIAS KAI EREVNAS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 485 625,00
Address
N PLASTIRA STR 100
70 013 IRAKLEIO
Greece

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Νησιά Αιγαίου Κρήτη Ηράκλειο
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Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 485 625,00

Participants (15)

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