SALUS addresses the growing security challenges of IoT systems by providing law enforcement with (i) new forensic investigation schemes and policies, and (ii) advanced IoT forensic tools for threat detection, evidence collection, and cross-agency collaboration, all while ensuring compliance with EU legal and ethical frameworks. To implement this, the project combines a novel Digital-Twin (DT) infrastructure for proactive threat simulation, a secure Software Defined Network-enabled IoT architecture (SDaaSS) acting as the backbone of the DT for dynamic policy enforcement, and AI-powered forensic capabilities for real-time IoT device detection, lawful evidence interception, and blockchain-based chain of custody. Validated through five diverse pilot use cases in collaboration with five (5) police authorities and two (2) critical infrastructure providers (hospitals and nuclear power plants), SALUS bridges technology and operational needs to enhance security in critical infrastructures and IoT ecosystems. The above technological developments will be closely accompanied by activities concerning: i) incorporation of legal and ethical aspects, including fundamental rights, privacy, personal data, etc, ii) integration of operational aspects, alignment with relevant EU cybersecurity policies, analysis of modus operandi, lawful evidence exploitation, and delivery of policy recommendations for tackling new and emerging forms of IoT-related crime, all aimed at improving police authorities’ understanding, iii) development of multi-dimensional, comprehensive practitioner training activities and joint exercises, tailored to the organization of operational-level hackathon activities, iv) Development of investigation, technological and security standards, and v) Establishment of a synergistic ecosystem among related national and EU-funded projects, as well as stakeholders from the law enforcement community.

ASGARD has a singular goal, contribute to Law Enforcement Agencies Technological Autonomy and effective use of technology. Technologies will be transferred to end users under an open source scheme focusing on Forensics, Intelligence and Foresight (Intelligence led prevention and anticipation). ASGARD will drive progress in the processing of seized data, availability of massive amounts of data and big data solutions in an ever more connected world. New areas of research will also be addressed. The consortium is configured with LEA end users and practitioners “pulling” from the Research and Development community who will “push” transfer of knowledge and innovation. A Community of LEA users is the end point of ASGARD with the technology as a focal point for cooperation (a restricted open source community). In addition to traditional Use Cases and trials, in keeping with open source concepts and continuous integration approaches, ASGARD will use Hackathons to demonstrate its results. Vendor lock-in is addressed whilst also recognising their role and existing investment by LEAs. The project will follow a cyclical approach for early results. Data Set, Data Analytics (multimodal/ multimedia), Data Mining and Visual Analytics are included in the work plan. Technologies will be built under the maxim of “It works” over “It’s the best”. Rapid adoption/flexible deployment strategies are included. The project includes a licensing and IPR approach coherent with LEA realities and Ethical needs. ASGARD includes a comprehensive approach to Privacy, Ethics, Societal Impact respecting fundamental rights. ASGARD leverages existing trust relationship between LEAs and the research and development industry, and experiential knowledge in FCT research. ASGARD will allow its community of users leverage the benefits of agile methodologies, technology trends and open source approaches that are currently exploited by the general ICT sector and Organised Crime and Terrorist organisations.

The proposed solution aims to deliver a descriptive and predictive data analytics platform and related tools using state-of-the-art machine learning and artificial intelligence methods to prevent, detect, analyse, and combat criminal activities. AIDA will focus on cybercrime and terrorism, by addressing specific challenges related to law enforcement investigation and intelligence. While cybercrime and terrorism pose distinct problems and may rely on different input datasets, the analysis of this data can benefit from the application of the same fundamental technology base framework, endowed with Artificial Intelligence and Deep Learning techniques applied to big data analytics, and extended and tailored with crime- and task- specific additional analytic capabilities and tools. The resulting TRL-7 integrated, modular and flexible AIDA framework will include LE-specific effective, efficient and automated data mining and analytics services to deal with intelligence and investigation workflows, extensive content acquisition, information extraction and fusion, knowledge management and enrichment through novel applications of Big Data processing, machine learning, artificial intelligence, predictive and visual analytics. AIDA system and tools will be made available to LEAs through a secure sandbox environment that aims to raise the technological readiness level of the solutions through their application in operational environment with real data.

Border authorities and Law Enforcement Agencies (LEAs) across Europe face important challenges in how they patrol and protect the borders. Their work becomes more problematic considering the heterogeneity of threats, the wideness of the surveyed area, the adverse weather conditions and the wide range of terrains. Although there are several research tools and works targeting these areas independently for border surveillance, nowadays border authorities do not have access to an intelligent holistic solution providing all aforementioned functionalities. Towards delivering such a solution, 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, capable of functioning both as standalone and in swarms, which will incorporate multimodal sensors as part of an interoperable network. The system will be equipped with adaptable sensing and robotic technologies that can operate in a wide range of operational and environmental settings. To provide a complete and detailed situational awareness picture that supports highly efficient operations, the network of sensors will include static networked sensors such as border surveillance radars, as well as mobile sensors customised and installed on board unmanned vehicles. To succeed implementing an operational solution, a number of supplementary technologies will also be applied that will enable the establishment of robust communication links between the command and control unit and the heterogeneous robots. On top of this, detection capabilities for early identification of criminal activities and hazardous incidents will be developed. This information will be forwarded to the command and control unit that will enable the integration of large volumes of heterogeneous sensor data and the provision of a quick overview of the situation at a glance to the operators, supporting them in their decisions.