The main task of the run-time application security is to protect modern applications and APIs. In this endeavor the solutions face a number of challenges: Download this whitepaper to learn how Wallarm solves the difficult task of effective application security by relying on AI and machine learning including a unique combination of hierarchical clusterization, statistical n-gram based models, recurrent neural networks and reinforcement learning.

Attack detection is critical for most security solutions, whether we are talking about a load balancer-based (NIDS, WAF), host-based or in-application solutions (HIDS, RASP). Interestingly, regardless of the differences in architecture and data flow, most solutions use similar detection principles and techniques. We will explore how the detection architecture evolved over time and how the new generation of detection logic, such as the architecture implemented by Wallarm, is principally different from that of the legacy solutions.