An Alert System to Avoid Financial Fraud
In the modern information society online transactions
are an important part of our daily lives. In this work we present an
alert system that determines the current threat level of financial
fraud in the internet. We use data from different sources and
off-the-shelf machine learning algorithms to compute the current
threat level. Based on the threat level our alert system issues
alerts to raise users awareness of current attack vectors. We
tested our approaches with real world online banking frauds. Our
preliminary results suggest that this mechanisms can be effectively
used to warn users about the current threat situation and therefore
help avoiding financial fraud.
are an important part of our daily lives. In this work we present an
alert system that determines the current threat level of financial
fraud in the internet. We use data from different sources and
off-the-shelf machine learning algorithms to compute the current
threat level. Based on the threat level our alert system issues
alerts to raise users awareness of current attack vectors. We
tested our approaches with real world online banking frauds. Our
preliminary results suggest that this mechanisms can be effectively
used to warn users about the current threat situation and therefore
help avoiding financial fraud.
