A new machine-learning system from the Computer Science and Artificial Intelligence Laboratory identifies “serial hijackers” of internet IP addresses, Adam Conner-Simons tells SciTechDaily.
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.
“Network operators normally have to handle such incidents reactively and on a case-by-case basis, making it easy for cybercriminals to continue to thrive,” says lead author Cecilia Testart, a graduate student at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who will present the paper at the ACM Internet Measurement Conference in Amsterdam on October 23, 2019. “This is a key first step in being able to shed light on serial hijackers’ behavior and proactively defend against their attacks.”
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