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:

• Applications are different both in structure and in content. Things that are harmful to one application may be perfectly normal for another.
• User behavior varies between both the applications and the individual application functions. For example several login calls every second may indicate a credential stuffing attack, while several data layer queries per second may be a normal function of building a correlated data set.
• The number of known attacks keeps growing, with attack patterns (or signatures) sometimes being hidden within nested protocols
• Straightforward implementation of attack detection based on signatures often results in a high rate of false positives and false negatives.

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.