Palo Alto Networks, Inc.

Isaac Schnitzer

Liron Levin

Elad Shuster

Ory Segal

Pavel Novik

Dima Stopel

John Morello

Itay Abramowsky

Michael Velbaum

A cybersecurity appliance monitoring application traffic to a web application programming interface (API) dynamically updates tree structures for the web API using the application traffic. An API tree generator generates batches of API trees from paths indicated in the application traffic. An API tree merger/pruner updates the generated batches of API trees with various merging, pruning, compacting, and malicious detection operations on the generated batches of API trees. The cybersecurity appliance implements the updated API trees with an API agent that filters the application traffic prior to processing by the web API.

Tree-based learning of application programming interface specification

A configuration of a cloud application exposed via a public IP address is duplicated with modifications to include a private IP address to expose the application internally. The original configuration is updated so that external network traffic sent to the application is redirected to and distributed across agents running on nodes of a cloud cluster by which web application firewalls (WAFs) are implemented. A set of agents for which the respective WAFs should inspect the redirected network traffic are selected based on cluster metrics, such as network and resource utilization metrics. The redirected network traffic targets a port allocated to the agents that is unique to the application, where ports are allocated on a per-application basis so each of the agents can support WAF protection for multiple applications. Network traffic which a WAF allows to pass is directed from the agent to the application via its private IP address.

Dynamically scalable application firewall deployment for cloud native applications

A system and method for cloud native virtual machine (VM) runtime protection. The method includes creating a normal behavior model for a cloud native VM by training a machine learning model using a training data set including training activities performed by the cloud native VM, the cloud native VM being configured to provide at least one service, wherein the normal behavior model defines at least one capability of each service based on a set of capabilities for respective known services stored within a library of service-to-capability mappings, wherein each capability of a service indicates a plurality of discrete behaviors required by the service; and monitoring an execution of the cloud native VM to detect a deviation from the normal behavior model, wherein the deviation is caused by at least one abnormal behavior of one of the services that is not among the discrete behaviors defined in capabilities for the service.

Cloud native virtual machine runtime protection

To dynamically determine and apply WAF protections for an application deployed to the cloud, exposed entities, are identified. The identified entities are further evaluated to determine whether the application is eligible for WAF protection based on whether the application uses a protocol that is compatible with WAF protection. If the application is eligible for WAF protection, after instantiating a WAF, WAF protections that should be enabled or disabled are determined based on characteristics of the application that are identified at runtime. The WAF can then be configured based on the identified protections such that those which are pertinent to the application will be enabled, while those which are not applicable to the application and thus will not be used are disabled. As a result, security provided by the WAF for a cloud application is tailored to the application based on information about the application gathered in the cloud deployment environment.

Growing community of inventors

Raanana, Israel

Isaac Schnitzer