The world has seen numerous major cyber attacks in the past couple of years, with targets ranging from government agencies to health insurers to entertainment companies and even Panamanian law firms. A group of scientists at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) are working to create a line of defense against these threats to privacy and security. They've developed an AI that can detect attacks on networks as they happen, 85 percent of the time. Our biggest ever edition of TNW Conference is fast approaching! AI2, short for Artificial Intelligence Squared, looks at data to detect suspicious activity.

They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx has developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

A new artificial intelligence (AI) system developed by MIT researchers promises to offer increased threat detection capabilities and reduce false positive rates, boosting incident response and productivity in the security world. The team, based at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), detailed in the paper AI2: Training a big data machine to defend [PDF], how the new platform achieves three times higher prediction capabilities, and is able to deliver significantly fewer false positive rates than current analytics models. The team showcased the AI2 platform last week at the IEEE International Conference on Big Data Security, and released the study to the public earlier today. The paper explains how the tool combines AI with'analyst intuition' to create a learning model whereby intermittent human analyst feedback is layered into a continuous unsupervised machine learning system. "You can think about the system as a virtual analyst," commented CSAIL research scientist Kalyan Veeramachaneni, who designed AI2 alongside PatternEx chief data scientist and former CSAIL researcher, Ignacio Arnaldo. "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," he added.

Diplomats from around the world met in Geneva last week for the United Nations' third Informal Expert Meeting on lethal autonomous weapons systems (LAWS), commonly dubbed "killer robots". Their aim was to make progress on deciding how, or if, LAWS should be regulated under international humanitarian law. A range of views were expressed at the meeting, from Pakistan being in favour of a full ban, to the UK favouring no new regulation for LAWS, and several positions in between. Despite the range of views on offer, there was some common ground. It is generally agreed that LAWS are governed by international humanitarian law.

Today's security systems usually fall into one of two categories: human or machine. So-called "analyst-driven solutions" rely on rules created by living experts and therefore miss any attacks that don't match the rules. Meanwhile, today's machine-learning approaches rely on "anomaly detection," which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway. But what if there were a solution that could merge those two worlds? What would it look like?

"Today's security systems usually fall into one of two categories: man or machine," Adam Conner-Simon from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) wrote in a post on the MIT News site. "So-called'analyst-driven solutions' rely on rules created by human experts and therefore miss any attacks that don't match the rules," he said. "Meanwhile, today's machine-learning approaches rely on'anomaly detection,' which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway." The MIT and PatternEx platform attempts to merge those two approaches. AI2 predicts attacks by combing through data and detecting suspicious activity by clustering it into meaningful patterns using unsupervised machine learning, according to researchers at MIT.

A team of MIT researchers created an artificial intelligence system called AI2 that can help stop cyberattacks. The AI is designed to review data from tens of millions of log lines each day and look for anything suspicious. When it finds something out of the ordinary, it hands off the information to a human that checks for any signs of a breach. "You can think about the system as a virtual analyst," said research lead Kalyan Veeramachaneni. "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."

Neither humans nor A.I. 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 impressive results. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx have developed a new platform called A.I.2 that can detect 85 percent of attacks. It also reduces the number of "false positives" -- nonthreats 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.

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 percent of attacks. It also reduces the number of "false positives" -- nonthreats 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.

Using a system that MIT is calling AI2, which was developed by the institute's Computer Science and Artificial Intelligence Laboratory, researchers have made it easier for humans to detect network breaches. Finding the evidence of a compromised network is a daunting take for security experts, at least for humans. The system MIT has developed doesn't sleep and can sift through millions of log lines looking for abnormalities before bringing them to an analyst's attention. After AI2 has found an anomaly following a review of data, it points out abnormalities to a human who takes over and has a thorough look at AI2's findings. According to the researchers, this human/AI team identified just shy of 90% of attacks while saving the human component hours and hours of time by not chasing after false leads.