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Machine learning for hunting Cybercriminals

Hijacking IP addresses have always been a popular form of cyber-attack. This is done for various reasons like sending spam, malware, stealing Bitcoins.  According to a report, It was found that in 2017 alone, routing incidents affected more than 10 percent of all the world’s routing domains.

Many developments have been done to detect IP hijacks when they’re already in process. But now things have changed with new technology, We could predict these incidents in advance by tracing things back to the hijackers themselves.  

With the help of a new machine-learning system developed by researchers at the University of California at San Diego (UCSD) and MIT. They have trained their system to be able to identify roughly 800 suspicious networks.

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) and believes that this technology can help to cut down the crime.

Testart said that the one challenge that they faced in developing the system was the events that may look like IP hijacks can be a result of human error. To solve this, the team manually jump in to identify false positives.

As people with time are increasingly relying on the Internet for their critical transactions, Testart fears that IP hijacking’s issues to get only worse. But she is also hopeful that it could be made more difficult by new security measures and the new mechanisms getting developed like this.

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