In Brief What if we could Predict when a cyber attack is going to occur before it actually happens and prevent it? Security researchers at MIT have developed a new Artificial Intelligence-based cyber security platform, called'AI2,' which has the ability to predict, detect, and stop 85% of Cyber Attacks with high accuracy. Cyber security is a major challenge in today's world, as government agencies, corporations and individuals have increasingly become victims of cyber attacks that are so rapidly finding new ways to threaten the Internet that it's hard for good guys to keep up with them. A group of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are working with machine-learning startup PatternEx to develop a line of defense against such cyber threats. The team has already developed an Artificial Intelligence system that can detect 85 percent of attacks by reviewing data from more than 3.6 Billion lines of log files each day and informs anything suspicious.

An anonymous reader writes: MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) says that while many'analyst-driven solutions' rely on rules created by human experts and therefore may miss attacks which do not match established patterns, a new artificial intelligence platform changes the rules of the game. The platform, dubbed AI Squared (AI2), is able to detect 85 percent of attacks -- roughly three times better than current benchmarks -- and also reduces the number of false positives by a factor of five, according to MIT. The latter is important as when anomaly detection triggers false positives, this can lead to lessened trust in protective systems and also wastes the time of IT experts which need to investigate the matter. AI2 was tested using 3.6 billion log lines generated by over 20 million users in a period of three months. The AI trawled through this information and used machine learning to cluster data together to find suspicious activity.

In Brief What if we could Predict when a cyber attack is going to occur before it actually happens and prevent it? Security researchers at MIT have developed a new Artificial Intelligence-based cyber security platform, called'AI2,' which has the ability to predict, detect, and stop 85% of Cyber Attacks with high accuracy. Cyber security is a major challenge in today's world, as government agencies, corporations and individuals have increasingly become victims of cyber attacks that are so rapidly finding new ways to threaten the Internet that it's hard for good guys to keep up with them. A group of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are working with machine-learning startup PatternEx to develop a line of defense against such cyber threats. The team has already developed an Artificial Intelligence system that can detect 85 percent of attacks by reviewing data from more than 3.6 Billion lines of log files each day and informs anything suspicious.

MIT scientists have built a hybrid human/artificial intelligence (AI) machine that they claim can learn how to detect 85% of cyber attacks – that's roughly three times better than previous benchmarks – while reducing false positive rates by a factor of 5. Nitesh Chawla, professor of computer science at Notre Dame University, said in a statement from MIT that the machine "has the potential to become a line of defense against attacks such as fraud, service abuse and account takeover." Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx demonstrated the platform, called AI2, in a paper titled "AI2: Training a big data machine to defend". As the researchers describe the current state of the art, today's security systems are typically driven by either humans – so-called "analyst-driven solutions" – or by machine. The problem with security systems based on fixed rules is that they miss attacks that don't match those rules. Machine-learning approaches, as the name suggests, rely on an adaptive process that can trigger annoying numbers of false positives.

Computer scientists from MIT and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.

Researchers from the Massachusetts Institute of Technology have created an AI system that can predict a cyberattack before it happens in 85% of incidents. 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.MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. Part of the challenge of merging human- and computer-based threat detection has been the manual labeling of data for algorithms.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. It then reports this activity to a human analyst who can then judge if there's an actual attack.With that feedback, it takes on board whether or not it should be classifying the events as attacks or not, then refines its internal models.According to Engadget, Kaylan Veermachaneni, co-creator of the system, said that one should think of the new system as a virtual analyst. In the near future the industry and federal regulators will need to figure out a balance between the need of cyber security and protecting consumers' privacy. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway.And the more data it analyses, the more accurate it becomes.

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), together with researchers from security firm PatternEx, has revealed a new AI (Artificial Intelligence) system called AI2, which can detect 85 percent of cyber-attacks, with false positives rates five times smaller than existing solutions. The new system doesn't rely entirely on artificial intelligence (AI), but also on user input, something that researchers call analyst intuition (AI), hence its name of AI2. Researchers said they fed AI2 with over 3.6 billion lines of log files, allowing the system to scan the content with unsupervised machine-learning techniques. At the end of each day, the system presents its findings to a human operator, who then confirms or dismisses security alerts. This human feedback is then incorporated into AI2's learning system and used the next day for analyzing new logs. After their tests had concluded, MIT and PatternEx researchers said AI2 achieved an 85 percent accuracy rate in detecting cyber-attacks, which is 2.92 times better than similar automated cyber-attack detection systems used today.

MIT scientists have built a hybrid human/artificial intelligence (AI) machine that they claim can learn how to detect 85% of cyber attacks – that's roughly three times better than previous benchmarks – while reducing false positive rates by a factor of 5. Nitesh Chawla, professor of computer science at Notre Dame University, said in a statement from MIT that the machine "has the potential to become a line of defense against attacks such as fraud, service abuse and account takeover." Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx demonstrated the platform, called AI2, in a paper titled "AI2: Training a big data machine to defend". As the researchers describe the current state of the art, today's security systems are typically driven by either humans – so-called "analyst-driven solutions" – or by machine. The problem with security systems based on fixed rules is that they miss attacks that don't match those rules. Machine-learning approaches, as the name suggests, rely on an adaptive process that can trigger annoying numbers of false positives.

Scientists at Massachusetts Institute of Technology (MIT) claim they have created an AI that can detect 85% of cyber attacks -- albeit with the help of humans. The "AI2" algorithm, developed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and machine learning startup PatternEx, can reportedly detect cyber attacks three times more effectively than today's current systems. AI2 has been tested on 3.6 billion pieces of data, known as "log lines," which were created over a three month period by millions of people. In order to predict attacks, AI2 scans sets of data and identifies suspicious activity. It does this by clustering the data into meaningful patterns using unsupervised machine-learning, according to MIT.

Computer scientists from the Michigan Institute of Technology (MIT) and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.