AIDA will develop a Big Data Analysis and Analytics framework equipped with a complete set of effective, efficient and automated data mining and analytics solutions to deal with standardised investigative workflows, extensive content acquisition, information extraction and fusion, knowledge management and enrichment through novel applications of Big Data processing, Machine Learning, AI and predictive and visual analytics. It will do so in a way that ensures societal benefits and consequences are integral part of design and deployment efforts.
ANITA's primary goal is twofold: a) To boost the LEA's investigation process and to significantly increase their operational capabilities, by introducing a set of innovative tools for efficiently addressing online illegal trafficking challenges (namely online data source analysis, blockchain analysis, Big Data analytics, knowledge modelling, incorporation of human cognitive function in the analysis pipelines, user-oriented intelligence applications), and b) To significantly facilitate the novice officers training process and to optimize the learning curve (by collecting, integrating and re-using knowledge from multiple expert officers and through the development of a recommendation functionality to transfer the acquired 'know-how' to the new officers).
CREST aims to equip LEAs with an advanced prediction, prevention, operation, and investigation platform by leveraging the IoT ecosystem, autonomous systems, and targeted technologies and building upon the concept of multidimensional integration and correlation of heterogeneous multimodal data streams (ranging from online content to IoT-enabled sensors) for a) threat detection and assessment, b) dynamic mission planning and adaptive navigation for improved surveillance based on autonomous systems, c) distributed command and control of law enforcement missions, d) sharing of information and exchange of digital evidence based on blockchain, and e) delivery of pertinent information to different stakeholders in an interactive manner tailored to their needs.
INFINITY's ambition is to become a flagship project against society’s most pressing cybercriminal, terrorist and hybrid threats. Synthesising the latest innovations in virtual and augmented reality, artificial intelligence and machine learning with big data and visual analytics, INFINITY will deliver an integrated solution that aims to revolutionise data driven investigations. Bringing together a strong representation from national and supranational agencies with an end user-driven design, it will directly address the core needs of contemporary law enforcement.
MAGNETO will revolutionize the capacity of Law Enforcement Agencies (LEAs) to deal with extreme volumes and diversity of data in order to accomplish highly-efficient crime prevention and investigation. The technologies and solutions developed by MAGNETO will permit LEAs to consistently process massive heterogeneous data in a more efficient manner, effectively enabling their transformation into solid and court-proof evidence.
PREVISION partners will take advantage of their capabilities, expertise and previously delivered research, together with already defined and emerging standards and best practices in Europe, so as to focus their resources and attention to the new elements and novel aspects of the project. The overall strategy in the execution of the PREVISION project is based on an iterative development methodology, which involves frequent software releases being made available to the LEA and practitioners end-users for testing and evaluation, resulting in keeping them continuously in the production loop. The PREVISION Platform will be deployed in 10 different demonstrations, managed by the different LEAs and practitioners of the consortium.
PROPHETS (Preventing Radicalisation Online through the Proliferation of Harmonised Toolkits) aims to counter online radicalisation, cybercrime, and cyberterrorism at their origins. Behavioural radicalisation can be seen as a driving force behind online criminal activities. PROPHETS seeks to identify, examine, and understand the various behavioural processes underlying such behaviour in order to recognize and comprehend the individual reasoning behind choosing to engage in such activities. Through the findings of this project, PROPHETS is developing new methods to detect, analyse, investigate, and fight the ever-emerging threats of online radicalisation, cybercrime, and cyberterrorism by addressing the very factors causing them.
PROTON is an innovative approach for a better understanding of the recruitment mechanisms in criminal and terrorist organisations. By combining social analyses with technological and computational sciences, the project aims at improving current prevention policies, as well as providing guidelines for policy makers and disparate end-users on a local, national and international scale. The ultimate goal is to tackle organised crime, terrorism and cybercrime through a reduction of their growth opportunities.
The vision of RED-Alert project is to develop a real-time system able to facilitate the timely identification of terrorism related content by summarizing data from social media. To fight the war against terror, LEAs are increasingly relying on social media intelligence, a new field of intelligence covering a wide range of applications, techniques and capabilities analyzing social media data. Addressing the needs and challenges, RED-Alert solution will cover a wide range of social media channels, in particular new channels such as Telegram and Periscope, which are increasingly used by terrorist groups to disseminate their content. The RED-Alert solution will allow LEAs to take coordinated action in real-time while preserving the privacy of citizens.
ROBORDER aims at developing and demonstrating a fully-functional autonomous border surveillance system with unmanned mobile robots including aerial, water surface, underwater and ground vehicles which will incorporate multimodal sensors as part of an interoperable network. ROBORDER's intention is to implement a heterogenous robot system and enhance it with detection capabilities for early identification of criminal activities at border and coastal areas along with marine pollution events. ROBORDER will collect heterogeneous data from several different resources such as thermal and optical cameras, passive radars and RF sensors originated from multiple vehicles/robots. The data will be semantically integrated in order to provide accurate decision support services to the corresponding authorities for border patrolling.
ROXANNE is an EU funded collaborative research and innovation project, aiming to unmask criminal networks and their members as well as to reveal the true identity of perpetrators by combining the capabilities of speech/language technologies and visual analysis with network analysis. ROXANNE collaborates with Law Enforcement Agencies (LEAs), industry and researchers to develop new tools to speed up investigative processes and support LEA decision-making. The end-product will be an advanced technical platform which uses new tools to uncover and track organized criminal networks, underpinned by a strong legal framework. The project consortium comprises 24 European organisations from 16 countries while 11 of them are LEAs from 10 different countries.
SHOTPROS aims to improve the training for European Police officers. The influence of psychological and contextual human factors (HFs) on the behaviour of decision-making and acting (DMA) of police officers under stress and in high-risk operational situations will be investigated. Based on the results, SHOTPROS will develop a HF-rooted training curriculum and a corresponding VR training solution to provide a comprehensive framework for practical training.
Video material collected and analysed by Law Enforcement Agencies (LEA) has become a critical component in legal investigations following major criminal acts and terrorist attacks. At the same time, the amount of video data available is continuously increasing. In spite of this growth, the whole video investigation work is still mostly carried out manually by the LEA officers. These current practices are too resource intensive to handle the huge and steadily increasing volume of videos that need to be analysed. Consequently, post-event extraction of vital first clues from videos meet unreasonable delays. In view of the needs, VICTORIA aims at creating a real breakthrough regarding functionality and usability of video analysis tools used for legal investigations.