False Positives Are a True Negative: Using Machine Learning to Improve Accuracy

Machine learning has grown to be one of the most popular and powerful tools in the quest to secure systems. Some approaches to machine learning have yielded overly aggressive models that demonstrate remarkable predictive accuracy, yet give way to false positives. False positives create negative user experiences that prevent new protection from deploying. IT personnel also find these false alarms disruptive when they are working to detect and eliminate malware. The Ponemon Institute recently reported that over 20 percent of endpoint security investigation spending was wasted on these false alarms.