Sicherheit und Prävention vor organisiertem Internet-Bestellbetrug für AnwenderInnen durch Maßnahmen der Digitalen-Forensik

Prevention is a key instrument in fighting cybercrime. Technological solutions that actively warn and protect consumers are an important addition to preventive measures. Machine learning (ML) methods in this field have increasingly improved in the past five years. In Austria, the ongoing flagship project MAL2 enables the classification of fake shops with detection rates of over 90%.

SINBAD derives the specific need for security research in the following areas: exploring gaps in the information space; providing specific advancements; proactively protecting consumers; and developing counter-narratives. A comprehensive analysis of fake shop fraud is carried out via (1) a technology-based monitoring with real persons for collecting data on listed products and prices, (2) a dark web research on modular fake shop systems, (3) analysis of ways and methods of group targeting by fraudulent eCommerce (advertising, social media, search engines) as well as assessing consumer needs. By proactively screening newly registered domains in the DACH region, the efficiency of the fake shop AI detector prototypes in terms of its ability in decreasing the window of opportunity (WoO) of fraudulent offers is evaluated and an interdisciplinary catalog of measures is derived.

The goals of the project are:

• Gain new insights into means of proactively detecting fake shops through user-centered methods, data-based models and the deepening of machine learning processes;
• Develop effective counter-narratives, through which consumers are strengthened and protected.

Facts:

• Projektbeginn: October 2020
• Projektdauer: 24 Monate
• Budget: ca. 260k EUR

Förderung: FFG KIRAS