Machine learning and math can’t trump smart attackers

itman · Apr 25, 2017

When you’ve been fighting black-hat hackers for decades, you learn a thing or two about them. Obviously, they are bad, and they like to play with code. But most importantly, they’re continually learning and you need to keep up if you want to protect your customers’ businesses from their sticky fingers.

Now, if we were a post-truth security vendor, we would talk a lot about how our machine learning makes us fit for the fight, or how mathematics can predict an attacker’s every move. We would also try to downplay the fact that even advanced technologies can be fooled by adversaries.

“No matter how smart a machine learning algorithm is, it has a narrow focus and learns from a specific data set.”

But at ESET, we value the truth. No matter how smart a machine learning algorithm is, it has a narrow focus and learns from a specific data set. By contrast, attackers possess so-called general intelligence and are able to think outside of the box. They can learn from context and benefit from inspiration, which no machine or algorithm can predict.

On top of that, the mathematics itself contradicts these post-truth claims – by referring to what’s known as an “undecidable problem”, i.e. determining whether a program will behave maliciously according to its external appearance – as demonstrated by the computer scientist who formulated the definition of computer virus, Fred Cohen.
Click to expand...

https://www.welivesecurity.com/2017/04/25/machine-learning-math-cant-trump-smart-attackers/

Log in or Sign up

Machine learning and math can’t trump smart attackers

itman Registered Member

Log in or Sign up

Machine learning and math can’t trump smart attackers

itman Registered Member

Useful Searches