Hello and good day/night to all colleagues!
Suppose I have data: attributes of requests (e.g. from history of online bank system)
- client IP (with IP info - continent, country, city, latitude, longitude)
- client agent (Operating System, Browser)
- some info about account (company, user)
- some info about the request (date, time, duration, size, from which url)
Could I build the fraud detection system based on these data analysis (or just anomaly detection systems)?
Are where any common or specifical approaches to such systems?
I'm interested in any information (books, articles, keywords to search, ideas, similar projects).
These attributes are being used to raise alerts and incidents in some of the commercial cyber security solutions such as Microsoft CloudApp Security (MCAS), Microsoft Azure AD Identity Protection (MAADIP), and Dark Web Security Solution from Vology. At least MS Solutions build up reputation for users and devices based on multiple attributes and then rate risk level of them or generate an alert or an incident using Machine Learning and AI. I suggest you take a look at the existing solutions and possible white papers of them.
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