An industry insider is making the case for behavioral biometrics to the wider world in a new essay for Forbes. Penned by BioCatch co-founder, CTO, and VP Avi Turgeman, the article proclaims that machine learning and behavioral biometrics are ‘a match made in heaven‘.
Addressing an audience that is likely pretty unfamiliar with the emerging technology, Turgeman usefully frames behavioral biometrics as a dynamic, rather than static, authentication technology. Classic biometrics like fingerprint, iris, and face scanning rely on physiological features that are broadly similar, and, on an individual level, pretty much unchanging. But there is wide variation in user behavior: Everyone is going to hold their phone, press buttons, type, and scroll in a slightly different way, which makes it that much harder for it to be spoofed by would-be hackers.
Of course, with BioCatch’s technology able to assess over 2,000 such parameters, that’s a lot to take in – and that’s where machine learning comes in. The system trains itself to develop a profile of user behavior, allowing for the development of a highly unique authentication signature for each user. Not only does this dynamic system offer sophisticated protection against hackers, it can also operate constantly and in the background as a user interacts with her device, which means that hackers can still be detected even if they take control of an account after an initial login session.
It’s a strong case for behavioral biometrics, and to underline his point, Turgeman cites a Research and Markets forecast predicting a CAGR of 44.1 percent for the machine learning market between 2017 and 2022, and a Mercator Advisory Group prediction that “[b]ehavioral biometrics will restructure the authentication landscape in the next five to eight years.” That should help to capture the attention of the Forbes audience as excitement about behvaioral biometrics and machine learning continues to grow in the digital security community.
(Originally posted on FindBiometrics)