Cyber Security: Why Machine Learning is Not Enough

Currently, there is a lot of talk about new analytical approaches in the field of cyber security. Anomaly detection and behavioral analytics are some of the overarching trends along with RTSI (Real Time Security Intelligence), which combines advanced analytical approaches with established concepts such as SIEM (Security Information and Event Management).

Behind all these changes and other new concepts, we find a number of buzzwords such as pattern-matching algorithms, predictive analytics, or machine learning. Aside from the fact that such terms frequently aren’t used correctly and precisely, some of the concepts have limitations by design, e.g. machine learning.

Machine learning implies that the “machine” (a piece of software) is able to “learn”. In fact this means that the machine is able to improve its results over time by analyzing the effect of previous actions and then adjusting the future actions.

One of the challenges with cyber security is the fact that there are continuously new attack vectors. Some of them are just variant of established patterns; some of them are entirely new. In an ideal world, a system is able to recognize unknown vectors. Machine learning per se doesn’t – the concept is learning from things that have gone wrong.

This is different from anomaly detection which identifies unknown or changing patterns. Here, the new is something which is identified as an anomaly.

Interestingly, some of the technologies where marketing talks about “machine learning” in fact do a lot more than ex-post-facto machine learning. Frequently, it is not a matter of technology but of the wrong use of buzzwords in marketing. Anyway, customers should be careful about buzzwords: Ask the vendor what is really meant by them. Any ask yourself whether the information provided by the vendor really is valid and solves your challenge.


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