{"id":22052,"date":"2019-07-17T08:00:35","date_gmt":"2019-07-17T06:00:35","guid":{"rendered":"https:\/\/www.cde.ual.es\/?p=22052"},"modified":"2019-07-16T09:00:51","modified_gmt":"2019-07-16T07:00:51","slug":"h2020-secure-societies-information-and-data-stream-management-to-fight-against-cybercrime-and-terrorism","status":"publish","type":"post","link":"https:\/\/www.cde.ual.es\/en\/h2020-secure-societies-information-and-data-stream-management-to-fight-against-cybercrime-and-terrorism\/","title":{"rendered":"H2020: Secure societies: Information and data stream management to fight against (cyber)crime and terrorism"},"content":{"rendered":"

This call (SU-FCT03-2018-2019-2020) aims to manage the flow of information and data to combat (cyber)crime and terrorism, and is part of the Secure societies – Protecting freedom and security of Europe and its citizens sub-programme of the Horizon 2020 Programme.<\/strong><\/em><\/p>\n

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The amount of data generated and gathered in the frame of (cyber)crime investigations increases exponentially, thereby creating a considerable challenge for law enforcement. The effectiveness of law enforcement action depends on capabilities to improve the quality of data, and to convert voluminous and heterogeneous data sets (images, videos, geospatial intelligence, communication data, traffic data, financial transactions related date, etc.) into actionable intelligence. These capabilities could be significantly enhanced by the use of domain-specific tools, i.e. Big Data analysis applications designed for the needs of crime investigators (pre-processing, processing and analysis, visualisation, etc.). Furthermore, predictive analytics would greatly benefit from open source intelligence gathering, social network and darknet data analysis, and allow for resource-efficient, effective and proactive law enforcement.<\/p>\n

Objective<\/h2>\n

Proposals are invited from consortia involving relevant security practitioners, civil society organisations, and the appropriate balance of IT specialists, psychologists, sociologists, linguists, etc. exploiting Big Data and predictive analytics that both<\/p>\n

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  1. \u00a0characterise trends in cybercrime and in cybercriminal organizations (based on a profound analysis of current and emerging cybercriminal organizational types and structures), and<\/li>\n
  2. enhance citizens’ security against terrorist attacks in places considered as soft targets, including crowded areas (stations, shopping malls, entertainment venues, etc.).<\/li>\n<\/ol>\n

    In 2020, proposals should address exclusively point b), with a focus on private operators. Although public authorities are primarily responsible for security, public-private cooperation is key in protecting public spaces. As an example, the first persons on the scene of a terrorist attack are often not police officers, but private security staff from local shops or transport operators. Moreover, public spaces are often owned and operated by private entities.<\/p>\n

    Proposals should lead to solutions developed in compliance with European societal values, fundamental rights and applicable legislation including in the area of privacy and protection of personal data. Societal aspects (e.g. perception of security, possible side effects of technological solutions, societal resilience) have to be addressed in a comprehensive and thorough manner.<\/p>\n

    Expected Impact:<\/p>\n

    Medium term:<\/strong><\/p>\n