Project

ANITA will design and develop an innovative knowledge-based user-centered cognitive investigation system for analysing heterogeneous (text, audio, video and image) online (Surface Web, Deep Web, Dark Nets) and offline (LEAs’ databases) resources for fighting illegal trafficking activities through an elegant combination of:

avant-garde data source analysis and blockchain technologies for the analysis of crypto-currency network and transactions (supporting the cases of Surface Web, Deep Web and Dark Nets);

advanced Big Data analytics tools for the automatic extraction and analysis of the vast amounts of multimodal multimedia content contained in the identified sources;

sophisticated methodologies for capturing, modelling, inferring, processing and storing knowledge in human understandable forms (e.g. expressive high-level semantic ontologies), including the case of connecting the connectionist learning paradigm with the symbolic learning one (i.e. extracting relevant and new knowledge from neural networks and formally storing it in the form of ontologies);

the development of a sophisticated, adaptive, cognitive user modelling framework that will capture, analyse, interpret, mimic and integrate key human cognitive and information processing functions for:
- incarnating the incorporation of human perception/cognition principles in the system processing pipelines (i.e. integrating the investigators/officers/ practitioners ‘in-the-loop’ of the overall analysis process)
- facilitate the transfer of domain knowledge/expertise from the expert users to the novice ones;

domain-related and user-oriented intelligence applications, able to provide users with interactive dashboards to explore, reconstruct and identify patterns for spatial, temporal and causal correlations among illegal trafficking events, entities and activities and to support decision-making processes for countermeasures to undertake. All the above functionalities will be coupled and reinforced by an in-depth interdisciplinary analysis of the online illegal trafficking phenomenon (including the study of reaction strategies and countermeasures) and a thorough analysis of the online resources with respect to social, ethical, legal and privacy issues of concern. The proposed system capabilities will be demonstrated in multiple relevant operational environments.

Main goals

ANITA’s primary goal is twofold:

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)

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).

Objectives

Over the recent years, online illegal trafficking activities have hugely elaborated and expanded so that to operate at global level with worldwide supply chains, production facilities and administrative offices, while their legal, economic and sustainability state is optimised. For tackling these emerging challenges, a significant part of LEAs’ efforts has been invested on training activities to equip officers and practitioners with the necessary knowledge and skills related to this emerging and continuously/rapidly evolving scenery. However, in order to efficiently understand the organisational structure, the exhibited behaviours/dynamics and their interconnections/interactions, it is of vital importance to collect and analyse all relevant open-source information in near real-time and to combine it with closed-source information provided by the LEAs. Another aspect that significantly adds to the complexity of the problem concerns the fact that often large political, religious and economic support networks support these online illegal trafficking activities, in order to receive funding for fulfilling their purposes or to gain political power. For this reason, the analysis of the underlying economic routes and networks can help LEAs to identify hidden relationships, recurrent strategies and emerging patterns in support of illegal activities. Building on the aforementioned analysis, some crucial radical changes in the criminal/illegal online activities landscape need also to be taken into account:

Criminal actors are proliferating (e.g. individuals, unstructured and informal organised criminal groups, structured and hierarchical organised crime, ethnic-based criminal groups, etc.), possessing a greater capacity to exploit a wide range of opportunities and resources, while maximising benefits and minimizing risks;

Organised criminal groups are continuously exploiting the interdependencies among the criminal activities they are already involved in, but they are also specialising in specific typologies of emerging crimes or even in specific segments of the criminal supply chain (Europol IOCTA 2016, SOCTA 2017);

Online trafficking activities are going “entrepreneurial”; in other words, they are organised in a managerial manner aiming to expand business activities by infiltrating into key legal and illegal new economic sectors/activities, on a global scale;

Surface Web, Deep Web and Dark Nets can be regarded as key crime-facilitators. They offer new opportunities to the organised criminal groups, assisting them: a) to cooperate in a more effective, efficient, anonymous (e.g. anonymising software such as P2P or Tor) and secure way, although dislocated in diverse countries; b) to manage criminal business-to-business (B2B) and business-to-consumer (B2C) activities, with anonymous online payments, through crypto-currencies (e.g. Bitcoin); c) to spread properly manipulated information to camouflage/conceal criminal activities, while deceiving online consumers; and d) to enlarge the crime-as-a-service activities and the related black market (e.g. Silk Road) in the Deep Web.

Among the most emblematic, emerging, large-scale and continuously evolving aspects of online illegal trafficking are the cases of:

counterfeit/falsified medicines (OTFM), drugs, Novel Psychoactive Substances (NPS),

Weapons and firearms,

terrorism funding.

The respective support networks are using different techniques to prevent them from being unveiled. The first strategy is to present them as legal as possible. As an example, novel psychoactive substances (NPS), the so called “legal highs”, are generated faster than the update rate of the blacklist for illegal drugs. Thus, trading and transporting of NPS is legal, even though, the psychoactive effect is often stronger, than existing illegal drugs. This “appear as legal” strategy is also followed by some specific charity organisations, which offer community services, but in fact they are organising worldwide fund raising activities for terrorism organisations. The second strategy is to build up an environment within which identification and censorship are impossible, instead of caring about legal compliance. The next generation of black markets will provide an infrastructure to set up anonymous companies, with invisible owners, anonymous management, employees and customer, like the Ethereum platform. The synergies between the trafficking activities concerning a) counterfeit/falsified medicine, drugs and NPS, b) weapons and firearms, and c) terrorism funding are considered as an emerging trend that, in the short term, could lead to new organized criminal arrangements as well as in an increasingly complex illegal market. Additionally, the aforementioned illegal trafficking activities will be further boosted by the next generation crypto currencies that will serve as a standard payment method for legal and illegal services, provided in the deep and dark web. They are expected to prove as a more reliable and less expensive alternative (EMCDDA) to anonymous payment systems.