Using Machine Learning to Detect IP Hijacking
This is interesting research:
In a BGP hijack, a malicious actor convinces nearby networks that the best path to reach a specific IP address is through their network. That’s unfortunately not very hard to do, since BGP itself doesn’t have any security procedures for validating that a message is actually coming from the place it says it’s coming from.
To better pinpoint serial attacks, the group first pulled data from several years’ worth of network operator mailing lists, as well as historical BGP data taken every five minutes from the global routing table. From that, they observed particular qualities of malicious actors and then trained a machine-learning model to automatically identify such behaviors.
The system flagged networks that had several key characteristics, particularly with respect to the nature of the specific blocks of IP addresses they use:
- Volatile changes in activity: Hijackers’ address blocks seem to disappear much faster than those of legitimate networks. The average duration of a flagged network’s prefix was under 50 days, compared to almost two years for legitimate networks.
- Multiple address blocks: Serial hijackers tend to advertise many more blocks of IP addresses, also known as “network prefixes.”
- IP addresses in multiple countries: Most networks don’t have foreign IP addresses. In contrast, for the networks that serial hijackers advertised that they had, they were much more likely to be registered in different countries and continents.
Note that this is much more likely to detect criminal attacks than nation-state activities. But it’s still good work.