A Hybrid AI System That Is x 3 Better Than Automated Systems
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.
The new system does not just rely on the artificial intelligence (AI), but also on human input, which researchers call Analyst Intuition (AI), which is why it has been given the name of Artificial Intelligence Squared or AI2.
How Does AI2 Work?
The system first scans the content with unsupervised machine-learning techniques and then, at the end of the day, presents its findings to human analysts.
The human analyst then identifies which events are actual cyber-attacks and which aren't. This feedback is then incorporated into the machine learning system of AI2 and is used the next day for analysing new logs.
The more data it analyses, the more accurate it becomes
In its test, the team demonstrated that AI2 is roughly 3 times better than similar automated cyber-attack detection systems used today. It also reduces the number of false positives by a factor of five.
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