Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an artificial intelligence (AI) platform that can ‘predict 85% of cyberattacks’, so long as it benefits from human input.
Its latest paper, AI2: Training a big data machine to defend, revealed that this unique approach is capable of delivering better results than machines or humans would be able to alone.
The collaborative effort sees the AI system take the lead – it ‘combs’ through data, highlighting the parts it considers to be suspicious and characteristic of cyberattacks.
Humans then take over, analysing the information provided by the system to then validate the findings. Feedback is then passed back to the system, helping improve AI2’s detection capabilities.
This process continues and with each iteration, its ability to accurately identify cyberattacks improves.
“You can think about the system as a virtual analyst,” explained Kalyan Veeramachaneni, a research scientist at CSAIL who co-developed the system.
“It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly.”
What is interesting about these findings is that it underscores the importance of human input, as a recent Wired article noted.
Speaking to the publication, Mr. Veeramachaneni explained that with cybersecurity evolving all the time, non-machine insight is something that cannot be replicated.
He said: “The attacks are constantly evolving. We need analysts to keep flagging new types of events. This system doesn’t get rid of analysts. It just augments them.”
