Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: The invention concerns API proxy based adaptive security. The invention implements adaptive security for API servers, while avoiding data bottlenecks and maintaining client experience. The invention provides methods and configurations for API security that may be employed at proxies for implementing routing decisions involving client messages received at said proxies. The invention also involves generating or collecting at proxies, log information that captures data corresponding to received client messages and responses from API servers—which log information correlates communications between clients, proxies and backend API servers, and includes data relevant for purposes generating API metrics and identifying anomalies and/or indicators of compromise.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: The invention enables high-availability, high-scale, high security and disaster recovery for API computing, including in terms of capture of data traffic passing through proxies, routing communications between clients and servers, and load balancing and/or forwarding functions. The invention inter alia provides (i) a scalable cluster of proxies configured to route communications between clients and servers, without any single point of failure, (ii) proxy nodes configured for implementing the scalable cluster (iii) efficient methods of configuring the proxy cluster, (iv) natural resiliency of clusters and/or proxy nodes within a cluster, (v) methods for scaling of clusters, (vi) configurability of clusters to span multiple servers, multiple racks and multiple datacenters, thereby ensuring high availability and disaster recovery (vii) switching between proxies or between servers without loss of session.

Abstract: The present disclosure relates to traffic monitoring through one or more access control servers configured configured for (i) routing server resource request messages to resource server(s), (ii) extracting information identifying a target server resource from data packets corresponding to one or more received server resource request messages, and (iii) selectively transmitting the received server resource request message to a resource server. The security server(s) is configured to receive a server resource request message data extracted from a server resource request message and initiate a first security response, wherein the initiated first security response is dependent on analysis of the server resource request message data.

Abstract: In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.

Abstract: In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: The invention enables high-availability, high-scale, high security and disaster recovery for API computing, including in terms of capture of data traffic passing through proxies, routing communications between clients and servers, and load balancing and/or forwarding functions. The invention inter alia provides (i) a scalable cluster of proxies configured to route communications between clients and servers, without any single point of failure, (ii) proxy nodes configured for implementing the scalable cluster (iii) efficient methods of configuring the proxy cluster, (iv) natural resiliency of clusters and/or proxy nodes within a cluster, (v) methods for scaling of clusters, (vi) configurability of clusters to span multiple servers, multiple racks and multiple datacenters, thereby ensuring high availability and disaster recovery (vii) switching between proxies or between servers without loss of session.

Abstract: The invention enables high-availability, high-scale, high security and disaster recovery for API computing, including in terms of capture of data traffic passing through proxies, routing communications between clients and servers, and load balancing and/or forwarding functions. The invention inter alia provides (i) a scalable cluster of proxies configured to route communications between clients and servers, without any single point of failure, (ii) proxy nodes configured for implementing the scalable cluster (iii) efficient methods of configuring the proxy cluster, (iv) natural resiliency of clusters and/or proxy nodes within a cluster, (v) methods for scaling of clusters, (vi) configurability of clusters to span multiple servers, multiple racks and multiple datacenters, thereby ensuring high availability and disaster recovery (vii) switching between proxies or between servers without loss of session.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for Application Programming Interface (API) based flow control and API based security at the application layer of the networking protocol stack. The invention additionally provides an API deception environment to protect a server backend from threats, attacks and unauthorized access.

Abstract: In some embodiments, a method includes receiving, at a processor of a server, a first application programming interface (API) call from a client device and providing an indication associated with the first API call as an input to a machine learning model such that the machine learning model identifies a set of parameters associated with a set of likely subsequent API calls. The method can further include receiving a second API call from the client device, identifying the second API call as an anomalous API call based on the second API call not meeting the set of parameters associated with the set of likely subsequent API calls, and sending a signal to perform a remedial action based on the identifying.

Abstract: The invention provides methods, computer program products, proxies and proxy clusters configured for forwarding, routing and/or load balancing of client requests or messages between multiple different APIs and/or multiple instances of an API. The invention further provides for efficient session information based routing of client requests for a target API, wherein multiple instances of the target API are simultaneously implemented across one or more API servers. The invention additionally enables separation of a control plane (i.e. control logic) and run time execution logic within a data plane within proxies in a proxy cluster, and also enables implementation of a plurality of data planes within each proxy—thereby ensuring security, high availability and scalability. An invention embodiment additionally implements two-stage rate limiting protection for API servers combining rate limiting between client and each proxy, and rate limiting between a proxy cluster and a server backend.

Abstract: The invention concerns API proxy based adaptive security. The invention implements adaptive security for API servers, while avoiding data bottlenecks and maintaining client experience. The invention provides methods and configurations for API security that may be employed at proxies for implementing routing decisions involving client messages received at said proxies. The invention also involves generating or collecting at proxies, log information that captures data corresponding to received client messages and responses from API servers—which log information correlates communications between clients, proxies and backend API servers, and includes data relevant for purposes generating API metrics and identifying anomalies and/or indicators of compromise.

Abstract: The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.