Cyberattack prediction to improve drastically

False positives are a scourge in cyberattack detection partly because of the way machine learning detects attacks. It’s done through what’s called anomaly detection where the artificial intelligence (AI) searches for code that isn’t as expected. That “tends to trigger false positives,” says MIT News, writing about a new AI platform that its scientists say will alleviate the trip-ups. The way they want to do it is to simply add humans to the mix. “Distrust of the system” means results have “to be investigated by humans, anyway,” MIT News says. INSIDER: Traditional anti-virus is dead: Long live the new and improved AVAI2, as the new system is called, merges analyst intuition with AI. The researchers believe they can obtain an 85 percent prediction rate with the combination. That’s “roughly three…

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