“Machine learning and behavioral analysis is one of the biggest trends in detecting anything and everything these days,” says Alexandru Balan, Chief Security Researcher at cybersecurity tech firm Bitdefender.
This idea was leveraged by startup tech company Dojo-Labs to create a smart-home Io T security solution.
“When it comes to Io T devices they were designed to do a very, very specific function,” says Yossi Atias, co-founder and CEO of the company.
And by no means can it be considered a complete solution by itself.
“[Machine learning] is going to be virtually everywhere,” says Veeramachaneni.
The same mechanics can be employed in security-related use cases, such as determining safe device behavior and general usage patterns, which can subsequently help to spot and block abnormal activity and potentially harmful behavior.
Already, several tech firms are drawing on this to offer solutions that enhance Io T security, especially in smart homes, where there are no defined security standards and practices.
The system generates events that indicate potential attacks.
The human investigates the events and determines whether the system was correct in its assessment or not.
“Machine learning is a critical component to developing Artificial Intelligence for Io T security,” says Uday Veeramachaneni, co-founder and CEO at Pattern Ex.
“The problem is that the Io T’s will be distributed massively and if there is an attack you have to react in real-time.” Most systems relying on machine learning and behavior analysis will gather information about the network and connected devices and subsequently seek everything that is out of normal.