"A Human-Machine Collaboration to Defend Against Cyberattacks"

Cybersecurity analysts' tasks aimed at stopping cyberattacks before they cause any damage are data-heavy, thus increasing the adoption of machine learning (ML) and systems powered by artificial intelligence (AI) to help perform such tasks. However, studies have shown that the ML systems come with problems like the frequent generation of false positives that can impact security analysts' productivity. The Massachusetts Institute of Technology's (MIT's) startup company, PatternEx, uses a system in which machine learning algorithms flag possible attacks and allows security analysts to provide feedback to the system. Feedback from human experts filters out false positives, leading to an increase in analyst productivity. Through fewer alerts, PatternEx's Virtual Analyst Platform can identify ten times more threats than a generic anomaly detection software program. This article continues to discuss how PatternEx's Virtual Analyst Platform can help security analysts as well as the collaboration between humans and machines in the approach to defend against cyberattacks.

Technology Org reports "A Human-Machine Collaboration to Defend Against Cyberattacks"