Race Against The Machine – Will ML Help Or Kill Security?

Machine Learning is being applied in many use cases of information technology, with varying, but sometimes mind-blowing success. Like any emerging tech, it can be used for better or worse when it comes to “cyber”.

The talk will examine the relations between ML and security:

• What security risks lie in the use of ML?
• Where and how can ML help attackers? Where not? And why (not)?
• Where and how can ML help defenders? Where not? And why (not)?
• What attack vectors on ML itself exist? Can these be countered with ML?

Only understanding the complex interrelation between different aspects of security and ML will allow us to create and use a technology that is beneficial in the end.

Required audience experience

Basic knowledge of ML is helpful as is an nterest in cybersecurity.

Objective of the talk

The audience will:

• Get a notion of the basic objectives of cybersecurity that are linked to ML
• Develop a deeper understanding of classes of attack (red) and defense (blue) techniques and the way the can or cannot be enhanced with ML
• Get an insight into security risks of ML itself

You can view David’s presentation below:

David Fuhr Mcubed presentation