Hijacking IP addresses is an increasingly popular form of cyber-attack. This is done for a range of reasons, from sending spam and malware to stealing Bitcoin. It’s estimated that in 2017 alone, routing incidents such as IP hijacks affected more than 10 percent of all the world’s routing domains. There have been major incidents at Amazon and Google and even in nation-states — a study last year suggested that a Chinese telecom company used the approach to gather intelligence on western countries by rerouting their internet traffic through China.

Existing efforts to detect IP hijacks tend to look at specific cases when they’re already in process. But what if we could predict these incidents in advance by tracing things back to the hijackers themselves?

That’s the idea behind a new machine-learning system developed by researchers at MIT and the University of California at San Diego (UCSD). By illuminating some of the common qualities of what they call “serial hijackers,” the team trained their system to be able to identify roughly 800 suspicious networks — and found that some of them had been hijacking IP addresses for years.

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詳見全文： MIT News

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