Abstract: A method by a web application layer proxy for predictively activating security rules to protect one or more web application servers from attacks by one or more web application clients. The method includes applying a set of security rules to web application layer requests received from the one or more web application clients that are intended for the one or more web application servers, determining a set of recently triggered security rules, where the set of recently triggered security rules includes those security rules in the set of security rules that were triggered within a most recent period of time, applying a prediction model to the set of recently triggered security rules to determine one or more security rules that are predicted to be triggered, and activating the one or more security rules.

Abstract: A method is described for a proxy to mitigate attacks from web application clients based on context of web application layer requests. The method includes receiving a plurality of web application layer requests from a web application layer client; aggregating a first set of requests from the plurality of web application layer requests, wherein the first set of requests are part of a first session; determining a profile based on the first set of requests, wherein the profile describes a baseline of expected behavior for a user of the web application layer client; and determining a first threat value associated with the first set of requests based on the first set of requests and the profile, wherein the first threat value describes the likelihood that the first set of requests are part of an attack on one or more web application servers.

Abstract: Systems and methods of detecting communication anomalies in a computer network, including: applying a machine learning (ML) algorithm on sampled network traffic, wherein the ML algorithm is trained with a training dataset comprising vectors to identify an anomaly when the ML algorithm receives a new input vector representing sampled network traffic, normalizing a loss determined by the ML algorithm based on the output of the ML algorithm for the new input vector being different from the output of the ML algorithm for the training dataset, and applying the ML algorithm to analyze the normalized loss to identify an anomaly based on at least one communication pattern in the sampled network traffic, allowing a model trained in one installation to serve as a base model in another installation by normalizing the loss vectors of each installation.

Abstract: A method by a web application layer proxy for predictively activating security rules to protect one or more web application servers from attacks by one or more web application clients. The method includes applying a set of security rules to web application layer requests received from the one or more web application clients that are intended for the one or more web application servers, determining a set of recently triggered security rules, where the set of recently triggered security rules includes those security rules in the set of security rules that were triggered within a most recent period of time, applying a prediction model to the set of recently triggered security rules to determine one or more security rules that are predicted to be triggered, and activating the one or more security rules.

Abstract: A method is described for a proxy to mitigate attacks from web application clients based on context of web application layer requests. The method includes receiving a plurality of web application layer requests from a web application layer client; aggregating a first set of requests from the plurality of web application layer requests, wherein the first set of requests are part of a first session; determining a profile based on the first set of requests, wherein the profile describes a baseline of expected behavior for a user of the web application layer client; and determining a first threat value associated with the first set of requests based on the first set of requests and the profile, wherein the first threat value describes the likelihood that the first set of requests are part of an attack on one or more web application servers.