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By using an algorithm to simultaneously study all three attacks , however , the technology can detect data correlations that otherwise would not be apparent to an unassisted human being . “ The algorithm may suggest that the attacker in all three scenarios was interested in profiting from natural resources , indicating that a single attacker was possibly at play — what we call a ‘ ground truth ,’” Ramsey says . “ By drawing this connection , we ’ re able to infer that the same threat actor might go after a similar entity engaged in natural resources .”
Machine learning can be a way to ferret out similarities and anomalies in different types of malicious behaviors such as these . And while , in theory , security specialists could undergo a similar analysis , algorithms have the capacity to draw these inferences much sooner and with greater accuracy .
It ’ s these same benefits of anomaly detection — and speed — that have compelled a global financial technology institution to use AI to help protect its customers against fraud . The financial services giant is familiar with biometric authentication tools , such as fingerprint and facial recognition software , yet machine learning presents a new opportunity to protect and provide value to customers . “ We ’ ve started to use an algorithm to examine how customers interact with their mobile devices ,” explains Nick Curcuru , data analytics and cyber security expert . “ Their interactions with the device ’ s keyboard , for instance , create a unique signature of typical behaviors , giving us the ability to paint a more refined profile of that person for verification purposes .”
Machine learning algorithms analyze these customer behaviors , or what Curcuru calls “ passive biometrics ,” to detect unusual patterns . If the algorithm suggests an atypical behavior that does not align with the customer ’ s profile , the information may indicate attempted fraud by a threat actor .
Curcuru points out that this potential fraud detection has to happen within a matter of nanoseconds so a “ go or no-go ” decision regarding the customer ’ s transaction can be made instantly . “ This is all about the customer . This is all about the experience to make things seamless . Make things frictionless .”
ILLUMINATING THE THIEF The security experts anticipate refined improvements in AI ’ s capabilities to fight cyber threats in the next three to five years . “ I believe we will see tremendous progress in the sophistication of the algorithms ,” Hans predicts . “ We have plans to build ever more robust threat models , possibly on an industry sector basis .”
Meanwhile , Secureworks plans to apply machine learning to other cyber security aims . “ The more we know about ground truths , the better we can apply that to other needs , such as whether or not a threat actor has stolen data ,” he explains . “ Right now , there ’ s typically no factual evidence to be sure that data has actually been stolen . AI can at least help narrow these odds .”
And , Ramsey adds , if information security providers can reach a consensus to work together on giving machine learning greater visibility , their collective clout will mount an impressive offense against the enemy .
“ We and other security firms using machine learning models have improved the accuracy of our threat detection ,” he says . “ Assuming we can collectively share our data insights , a significant shift in cyber risk management will be at hand . This is a potential game-changer that will go down as a pivotal moment in cyber security .” ■
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