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Detect Fraudulent Activities in dark web and clear web to protect your business
DEFRAUDify is an ITEA3-5 project with consortia in the Netherlands, Turkey, and Spain.
DEFRAUDify aims at businesses that encounter negative impacts of internet organised fraudulent behaviour. The tools will consist of a set of interoperable tools that jointly analyse suspicious behaviour and provide situational awareness.
The Dutch use cases are provided by Bunq and Hoffmann and have to do with monitoring and detecting unusual financial transactions, and performing a strategic risk assessment for companies.
To this end, a toolset of different modules will be developed and tested in the provided test environments.
The modules will focus on crawling, semantics, and contextualization technology, or a combination of these fields.
The combination of these modules allows end-users to take a systematic approach to security and fraud prevention, leading to more efficient use of time and better interoperability of different methods.
DEFRAUDify aims to develop tools that help private businesses to detect fraudulent behaviour on the internet.
These tools are partly based on existing tools that have been developed for Law Enforcement. They will be adapted to become relevant for private businesses as well.
Almende will actively develop a crawling module using agent-based technology; this module can be used to actively probe potentially fraudulent or malicious users on the dark web or clear web, with the goal of improving the rate of detection. Such activities are currently performed in an ad-hoc fashion and require the time of specialized people trained for such a task.
The project presents a novel application area for agent-based technologies and is interesting because it involves direct interaction with human actors, whereas in other projects such interaction was mainly facilitated through other software agents. By understanding and emulating human behaviour, we foresee a number of other applications in the fields of decision support and human-machine interaction.
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