“Not all Artificial Intelligence is created equal
As we move towards a future where we lean on cybersecurity much more in our daily lives, it’s important to be aware of the differences in the types of AI being used for network security.
Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars.
However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies.
Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).
Labeling VS Learning
Supervised Learning relies on a process of labeling in order to “understand” information.
The machine learns from labeling lots of data and is able to “recognize” something only after someone, most likely a security professional, has already labeled it, as it can not do so on its own.”