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Cybercriminals Change Tactics to Outwit Machine-Learning Defense
The rise in machine learning for security has forced criminals to rethink how to avoid detection.
1 Min Read
Cybercriminals know that defenders have begun using machine learning to keep users safe. In response, they are changing their tactics to outwit the defenses, and machine learning that leans heavily on historic loss patterns is especially vulnerable.
According to the "Q3 2018 DataVisor Fraud Index Report," which is based on an analysis of sample data from more than 40 billion events, fraud actors have changed their tools so that they can quickly adapt to new defenses put in place by companies. In a statement accompanying the report, DataVisor puts numbers to the quick adaptation, saying, "Out of the fraud signals detected, 36% were active for less than one day, and 64% for less than one week."
In addition, sophisticated fraud actors are now more likely to make use of private domains, conduct multilayer, staged attacks, and wait for days or weeks between those stages.
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