Menu
AI + humans = kick-ass cybersecurity

AI + humans = kick-ass cybersecurity

A new hybrid system from MIT can detect 85 percent of attacks

MIT's AI-based system combs through data and presents suspicious activity to human analysts. Credit: Kalyan Veeramachaneni/MIT CSAIL

MIT's AI-based system combs through data and presents suspicious activity to human analysts. Credit: Kalyan Veeramachaneni/MIT CSAIL

Neither humans nor AI has proven overwhelmingly successful at maintaining cybersecurity on their own, so why not see what happens when you combine the two? That's exactly the premise of a new project from MIT, and it's achieved some pretty impressive results.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a new platform called AI2 that can detect 85 per cent of attacks. It also reduces the number of "false positives" - non-threats mistakenly identified as threats - by a factor of five, the researchers said.

The system was tested on 3.6 billion pieces of data generated by millions of users over a period of three months. The researchers presented a paper summarising the project earlier this month at the IEEE International Conference on Big Data Security.

"You can think about the system as a virtual analyst," said CSAIL research scientist Kalyan Veeramachaneni, who developed AI2 with Ignacio Arnaldo, a chief data scientist at PatternEx and a former CSAIL postdoc. "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."

Even as fears abound regarding the job-replacing potential of artificial intelligence, it's becoming increasingly apparent that combining AI with human insight can deliver much better results than either side could produce alone. Just last week, for example, Spare5 released a new platform that applies a combination of human insight and machine learning to help companies make sense of unstructured data.

In the world of cybersecurity, human-driven techniques typically rely on rules created by living experts and therefore miss any attacks that don’t match the rules. Machine-learning approaches, on the other hand, rely on anomaly detection, which tends to trigger false positives that create distrust of the system and still end up having to be investigated by humans.

Creating cybersecurity systems that merge human and computer-based approaches isn't easy, though, partly because of the challenge of manually labeling cybersecurity data for the algorithms. For many tasks, such as visual recognition, labeling is just a matter of enlisting a few human volunteers on a crowdsourcing site like Amazon Mechanical Turk, but not many workers have the skills needed to apply labels like "DDOS" or "exfiltration attacks", Veeramachaneni said. "You need security experts."

Experts, meanwhile, tend to be short on time. Recognizing that constraint, AI2 uses machine learning first to find the most important potential problems; only then does it show the top events to analysts for labeling. On day one of its training, AI2 picks the 200 "most abnormal" events using unsupervised machine learning and gives them to the human expert, MIT explained. Those analysts then confirm which events are actual attacks, and the system incorporates that feedback into its models for the next set of data.

As the system improves over time, the number of events analysts must evaluate is reduced dramatically, MIT said.

"The more attacks the system detects, the more analyst feedback it receives, which in turn improves the accuracy of future predictions," Veeramachaneni says. "That human-machine interaction creates a beautiful, cascading effect."


Follow Us

Join the newsletter!

Or

Sign up to gain exclusive access to email subscriptions, event invitations, competitions, giveaways, and much more.

Membership is free, and your security and privacy remain protected. View our privacy policy before signing up.

Error: Please check your email address.

Tags AI

Featured

Slideshows

Reseller News Platinum Club celebrates leading partners in 2019

Reseller News Platinum Club celebrates leading partners in 2019

The leading players of the New Zealand channel came together to celebrate a year of achievement at the annual Reseller News Platinum Club lunch in Auckland. Following the Reseller News Innovation Awards, Platinum Club provides a platform to showcase the top performing partners and start-ups of the past 12 months.

Reseller News Platinum Club celebrates leading partners in 2019
Reseller News hosts alumnae breakfast for Women in ICT Awards

Reseller News hosts alumnae breakfast for Women in ICT Awards

Reseller News hosted its second annual alumnae breakfast for the Women in ICT Awards in New Zealand, designed to showcase the leading female leaders in the industry. Held at The Cordis in Auckland, attendees came together to hear inspiring keynotes and panel discussions, alongside high-level networking among peers. Photos by Gino Demeer.

Reseller News hosts alumnae breakfast for Women in ICT Awards
Reseller News Innovation Awards 2019: meet the winners

Reseller News Innovation Awards 2019: meet the winners

Reseller News honoured the standout players of the New Zealand channel in front of more than 480 technology leaders in Auckland on 23 October, recognising the achievements of top partners, emerging entrants and innovative start-ups.

Reseller News Innovation Awards 2019: meet the winners
Show Comments