Posts
 |
| Photo: Tom Obremski |
 |
| Photo: Thinkstock |
Security analytics are only as good as the data that feeds them. I’m not saying that advanced analytics such as machine learning and artificial intelligence (AI) aren’t amazingly powerful and increasing important for cybersecurity — they are. But unlocking the full potential of advanced analytics requires deep network insights: a combination of the right data, sufficient visibility into the details and context that we care about from a security perspective.
The Power of Deep Learning
I was reminded of this recently when I came across a research article on the use of deep learning for speech recognition. We’ve all witnessed firsthand just how good speech recognition has become, and it’s fundamentally changing how we interact with devices. Just look at the smart speakers finding their way into our homes. All we have to do is deploy these small cylindrical devices around the house, connect them and they will respond to voice commands, enabling us to play our favorite music, change the temperature on our smart thermostat or even order those last-minute holiday presents for family members.
However, this technology is dependent on being able to pick up our voices with sufficient audio clarity to discern what is being said. For this reason, these devices are designed to have multiple sensors deployed at different locations around our homes to detect what is spoken. After all, these devices are useless if they miss the phrase that begins a command.
Diving Deep Into Data
It’s no different in the security space. Analytics give us the power to find anomalous activity that humans simply couldn’t do in any reasonable amount of time. But this assumes that we have the data with the detail and clarity that we need in the first place.
We’ve been focusing heavily on deep network insights and data extraction with QRadar Network Insights to extend the capabilities of our security analytics.
Read more...
Source: Security Intelligence (blog)
