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.

Researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) have developed a cybersecurity system that combines human and machine-learning approaches to reduce cyber attacks and false positives. Named AI2 to signify that it merges artificial intelligence with "analyst intuition," the system was developed by Kalyan Veeramachaneni, a research scientist at CSAIL, and Ignacio Arnaldo, a former postdoctoral researcher at CSAIL who is now a chief data scientist at PatternEx. In tests, the researchers demonstrated that "AI2 can detect 85 percent of attacks, which is roughly three times better than previous benchmarks, while also reducing the number of false positives by a factor of five," according to a news release from CSAIL. Most modern cybersecurity systems use either analyst-driven solutions or machine-learning approaches. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.

Finding evidence that someone compromised your cyber defenses is a grind. Sifting through all of the data to find abnormalities takes a lot of time and effort, and analysts can only work so many hours a day. But an AI never gets tired, and can work with humans to deliver far better results. A system called AI2, developed at MIT's Computer Science and Artificial Intelligence Laboratory, reviews data from tens of millions of log lines each day and pinpoints anything suspicious. A human takes it from there, checking for signs of a breach.

Knowing a cyberattack's going to occur before it actually happens is very useful--but it's tricky to achieve in practice. Now MIT's built an artificial intelligence system that can predict attacks 85 percent of the time. Cyberattack spotters work in two main ways. Some are AI that simply looks out for anomalies in internet traffic. They work, but often throw up false positives--warnings about a threat when actually nothing's wrong. Other software systems are built on rules developed by humans, but it's hard to create systems like that which catches every attack.

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.

A NASA-developed air traffic control system for drones could take a major step forward this week when up to 24 drones take to the skies from locations across the U.S. in the agency's first coordinated test. Called UTM, for unmanned aircraft system traffic management, the platform is seen as a key safety system that would allow greater numbers of drones to fly in the sky and avoid mid-air collisions with piloted aircraft and other drones. It allows drone operators to enter a flight plan and request clearance for the flight. The system checks the plan for conflicts with other planned drone flights and accepts or rejects it. For example, a farmer could program a drone flight above her land, effectively reserving it for a certain period of time, and then carry out the flight in the knowledge that a delivery drone scheduled to arrive at the same time will automatically calculate a path to avoid a collision.

Jody Williams received the Nobel Peace Prize in 1997 together with the International Campaign to Ban Landmines for their central role in establishing the 1997 Mine Ban Treaty. The US-based political activist is known across the world for her efforts to enhance understandings of security and related issues in the world today. She is also the chair of the Noble Women's Initiative that she founded in 2006 together with five other women Nobel Peace laureates. She, along with 20 of her fellow Nobel Peace laureates have called for a preemptive ban on Lethal Autonomous Weapons Systems (LAWS)--weapons that could operate without human supervision once activated even in matters of killing human beings. The UN's Convention on Certain Conventional Weapons (CCW) held their third informal government's meet in Geneva from 11-15 April.