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 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 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 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 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 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 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 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 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 provide 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.