Machine Learning Stops Web Application Threats while Reducing False Positives

Cybercriminals are increasingly targeting public and internal web applications. Today, nearly half of all data breaches are caused by attacks targeting web application vulnerabilities. To protect your organization from such attacks, Web Application Firewalls (WAFs) are the gold standard. However, some organizations are reluctant to use these devices as they have a reputation for being very resource-intensive, especially when it comes to quickly addressing false positive detections in order to ensure that legitimate users and applications don't get blocked. The primary reason for the high number of false positive detections generated by most WAF solutions is the underlying core behavioral threat detection method being used.