System and method for real-time monitoring and remediation of network intrusion using an intelligent application programming interface
A system is provided for real-time monitoring and remediation of network intrusion using an intelligent application programming interface. In particular, the system may monitor and track, in real time, the various computing devices within a distributed networked system. The system may use one or more trained artificial intelligence models to analyze incoming network requests and detect anomalies within the body of network requests, and based on the analysis, implementing one or more countermeasures (e.g., request throttling, rate limiting, allocation of additional computing resources, and/or the like) in response. In some embodiments, the one or more AI models may be configured to generate intrusion mitigation and/or remediation plans in response to any detected anomalies. The output of the AI models may then be wrapped with additional data that may enhance the anomaly detection process.
1 . A system for real-time monitoring and remediation of network intrusion using an intelligent application programming interface, the system comprising:
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
monitoring, in real time, network data within a networked computing environment;
analyzing the network data using an artificial intelligence (“AI”) strategy model;
determining, based on the AI strategy model analyzing the network data, a possibility of overload of the networked computing environment;
initiating one or more remediation processes in response to the possibility of overload of the networked computing environment;
based on an output of the AI strategy model, appending, to the network data, additional metadata associated with each network transaction within the network data using an AI-based wrapper model, wherein the additional metadata comprises location, time frequency, endpoint access frequency, last attack time, and endpoint security policies;
based on an output of the wrapper model, performing anomaly detection of the network data using an AI-based anomaly detection model; and
based on performing the anomaly detection, generating and implementing a mitigation plan to address one or more identified anomalies within the network data.
2 . The system of claim 1 , wherein monitoring the network data comprises:
intercepting one or more authentication requests through a proxy object; and
performing one or more preliminary filtering processes on the one or more authentication requests, wherein the one or more preliminary filtering processes comprise rate limiting and IP address filtering of the one or more authentication requests.
3 . The system of claim 1 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises user based, application based, or device based rate limiting.
4 . The system of claim 1 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises temporary allocation of additional hardware resources for processing network requests, wherein the additional hardware resources comprise at least one of processing power, memory space, and network bandwidth.
5 . The system of claim 1 , wherein performing the anomaly detection comprises using a runtime-guided optimization process based on common intrusion patterns.
6 . The system of claim 1 , wherein performing the anomaly detection comprises processing the network data using thread-level speculation.
7 . A computer program product for real-time monitoring and remediation of network intrusion using an intelligent application programming interface, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to perform the steps of:
monitoring, in real time, network data within a networked computing environment;
analyzing the network data using an artificial intelligence (“AI”) strategy model;
determining, based on the AI strategy model analyzing the network data, a possibility of overload of the networked computing environment;
initiating one or more remediation processes in response to the possibility of overload of the networked computing environment;
based on an output of the AI strategy model, appending, to the network data, additional metadata associated with each network transaction within the network data using an AI-based wrapper model, wherein the additional metadata comprises location, time frequency, endpoint access frequency, last attack time, and endpoint security policies;
based on an output of the wrapper model, performing anomaly detection of the network data using an AI-based anomaly detection model; and
based on performing the anomaly detection, generating and implementing a mitigation plan to address one or more identified anomalies within the network data.
8 . The computer program product of claim 7 , wherein monitoring the network data comprises:
intercepting one or more authentication requests through a proxy object; and
performing one or more preliminary filtering processes on the one or more authentication requests, wherein the one or more preliminary filtering processes comprise rate limiting and IP address filtering of the one or more authentication requests.
9 . The computer program product of claim 7 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises user based, application based, or device based rate limiting.
10 . The computer program product of claim 7 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises temporary allocation of additional hardware resources for processing network requests, wherein the additional hardware resources comprise at least one of processing power, memory space, and network bandwidth.
11 . The computer program product of claim 7 , wherein performing the anomaly detection comprises using a runtime-guided optimization process based on common intrusion patterns.
12 . A computer-implemented method for real-time monitoring and remediation of network intrusion using an intelligent application programming interface, the computer-implemented method comprising:
monitoring, in real time, network data within a networked computing environment;
analyzing the network data using an artificial intelligence (“AI”) strategy model;
determining, based on the AI strategy model analyzing the network data, a possibility of overload of the networked computing environment;
initiating one or more remediation processes in response to the possibility of overload of the networked computing environment;
based on an output of the AI strategy model, appending, to the network data, additional metadata associated with each network transaction within the network data using an AI-based wrapper model, wherein the additional metadata comprises location, time frequency, endpoint access frequency, last attack time, and endpoint security policies;
based on an output of the wrapper model, performing anomaly detection of the network data using an AI-based anomaly detection model; and
based on performing the anomaly detection, generating and implementing a mitigation plan to address one or more identified anomalies within the network data.
13 . The computer-implemented method of claim 12 , wherein monitoring the network data comprises:
intercepting one or more authentication requests through a proxy object; and
performing one or more preliminary filtering processes on the one or more authentication requests, wherein the one or more preliminary filtering processes comprise rate limiting and IP address filtering of the one or more authentication requests.
14 . The computer-implemented method of claim 12 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises user based, application based, or device based rate limiting.
15 . The computer-implemented method of claim 12 , wherein the one or more remediation processes in response to the possibility of overload of the networked computing environment comprises temporary allocation of additional hardware resources for processing network requests, wherein the additional hardware resources comprise at least one of processing power, memory space, and network bandwidth.
16 . The computer-implemented method of claim 12 , wherein performing the anomaly detection comprises using a runtime-guided optimization process based on common intrusion patterns.
17 . The computer-implemented method of claim 12 , wherein performing the anomaly detection comprises processing the network data using thread-level speculation.