Abstract: 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.

Abstract: An apparatus includes a physical network adapter and one or more processors. The physical network adapter is configured to communicate over a network. The one or more processors are configured to host a Virtual Machine (VM) that runs software processes, to run a network stack of the VM, the network stack enabling the software processes to communicate network traffic over the network via the physical network adapter, to program the network stack to mirror at least a selected part of the network traffic of one or more of the software processes, and, using the programmed network stack, to mirror at least the selected part of the network traffic for inspection.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Abstract: A method of bot detection in a computer network leverages a machine learning system. The machine learning system receives a fingerprint derived at a server, the server having extracted a set of transport layer security parameters received from a client and processed the set parameters into the fingerprint. Based at least in part on the fingerprint, the learning system determines whether the client is likely to be a bot as opposed to a human user. The system generates and returns to the server as score having a first value when the fingerprint is determined to be associated with a good client, and having a second value when the fingerprint is determined to be associated with a bot. Based on the score received from the machine learning system, the server takes a configured action with respect to the client.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of bot detection in a computer network leverages a machine learning system. The machine learning system receives a fingerprint derived at a server, the server having extracted a set of transport layer security parameters received from a client and processed the set parameters into the fingerprint. Based at least in part on the fingerprint, the learning system determines whether the client is likely to be a bot as opposed to a human user. The system generates and returns to the server as score having a first value when the fingerprint is determined to be associated with a good client, and having a second value when the fingerprint is determined to be associated with a bot. Based on the score received from the machine learning system, the server takes a configured action with respect to the client.

Abstract: 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.

Abstract: This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Abstract: This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.