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).
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.
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.