Aipatent Drafting Product

AI-BASED SYSTEM AND METHOD FOR INTELLIGENT API GATEWAY TRAFFIC CONTROL

TITLE OF THE INVENTION

AI-BASED SYSTEM AND METHOD FOR INTELLIGENT API GATEWAY TRAFFIC CONTROL

CROSS-REFERENCE TO RELATED APPLICATIONS

Not Applicable.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates generally to the field of network traffic management and, more specifically, to systems and methods for intelligently controlling traffic at an Application Programming Interface (API) gateway using artificial intelligence.

Description of the Related Art

Application Programming Interfaces (APIs) have become the backbone of modern software architecture, enabling communication and data exchange between disparate services, particularly in microservices and cloud-native environments. The API gateway serves as a critical entry point, managing, securing, and mediating all incoming API requests to the appropriate backend services.

Conventional API gateways typically rely on static, rule-based configurations for traffic control. For example, administrators may define fixed rate limits (e.g., 100 requests per minute per user) or simple routing rules. While effective for predictable traffic patterns, these static approaches have significant limitations in today's dynamic and complex digital ecosystems. They are inherently reactive, meaning they can only respond to issues like traffic surges or security threats after they have occurred. This can lead to service degradation, outages, and a poor user experience. Furthermore, manually adjusting these rules to account for evolving traffic patterns is a cumbersome, error-prone, and inefficient process that does not scale well.

The proliferation of microservices has exacerbated these challenges. A single user action on a client application can trigger a cascade of dozens of API calls to various backend services, creating highly complex and often unpredictable traffic flows. Malicious activities, such as Distributed Denial of Service (DDoS) attacks, have also grown more sophisticated, often mimicking legitimate traffic to bypass simple, static security rules.

Therefore, a need exists for a more advanced, intelligent, and proactive approach to API gateway traffic management. A system that could predict traffic patterns, anticipate potential issues like service overloads or security breaches, and automatically apply precise control actions in real-time would represent a significant technological advancement. Such a system would enhance the reliability, security, and performance of API-driven applications, moving beyond the limitations of the current state of the art.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a system and method for intelligent API gateway traffic control that overcomes the limitations of prior art systems. The invention utilizes a layered, AI-driven architecture to achieve precise prediction and proactive control of API traffic.

One objective of the invention is to provide a system that ingests and processes a wide variety of real-time and historical data to build a comprehensive understanding of traffic patterns. This system comprises four core layers: a feature processing layer, a predictive analysis layer, a decision control layer, and a feedback optimization layer.

The feature processing layer is configured to receive raw data from various sources, such as API gateway logs, server performance metrics, and network data. It cleans, transforms, and engineers this raw data into a set of meaningful features suitable for consumption by machine learning models.

The predictive analysis layer utilizes one or more machine learning models to analyze the features and generate predictions about future traffic states. These predictions may include forecasts of traffic volume, identification of anomalous request patterns indicative of a security threat, or predictions of impending resource exhaustion on backend services.

The decision control layer receives these predictions and translates them into specific, actionable control policies. Based on the predicted conditions, this layer can generate commands to dynamically adjust rate limits, intelligently reroute traffic to healthier service instances, proactively adjust caching strategies, or block malicious actors. These commands are then executed by the API gateway.

The feedback optimization layer completes the intelligent control loop. It monitors the actual outcomes of the applied control decisions and compares them against the predicted outcomes. This feedback is used to continuously retrain and refine the machine learning models in the predictive analysis layer and the policy logic in the decision control layer, ensuring the system adapts and improves its performance over time.

In one embodiment, a system for intelligent traffic control of an API gateway is disclosed. The system includes a processor and a memory storing instructions. When executed, these instructions implement the four-layer architecture. The feature processing layer processes data into features like request rates and payload sizes. The predictive analysis layer, using models such as Long Short-Term Memory (LSTM) or Isolation Forest, predicts future traffic anomalies. The decision control layer generates control commands, such as dynamic rate limiting or intelligent routing policies. The feedback optimization layer updates the predictive models based on the efficacy of the control commands.

In another embodiment, a computer-implemented method for intelligent traffic control is disclosed. The method involves the steps corresponding to the functions of the four layers: processing raw data into features, predicting future traffic states using the features, generating control decisions based on the predictions, applying the decisions to an API gateway, and updating the predictive models based on the observed outcomes.

The present invention provides a significant advantage over the prior art by replacing static, reactive control mechanisms with a dynamic, predictive, and self-optimizing system. This leads to enhanced API availability, improved security against sophisticated threats, optimized resource utilization, and a superior end-user experience.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a system architecture diagram illustrating the intelligent API gateway traffic control system and its core layers, according to one embodiment of the invention.

Text-Based Representation for FIG. 1:

FIG. 2 is a flowchart illustrating a method for intelligent API gateway traffic control, according to one embodiment of the invention.

Text-Based Reesentation for FIG. 2:

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is presented to enable any person skilled in the art to make and use the invention. For purposes of explanation, specific nomenclature is set forth to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that these specific details are not required to practice the invention. Descriptions of specific applications are provided only as representative examples. Various modifications to the preferred embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the scope of the invention. The present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest possible scope consistent with the principles and features disclosed herein.

Referring now to the drawings, FIG. 1 illustrates a system architecture diagram of the intelligent API gateway traffic control system 100. The system 100 is communicatively coupled with an API Gateway 102, which serves as the primary ingress point for API requests from clients to a plurality of Backend Services 106. The system 100 continuously processes data from various Data Sources 104 to intelligently manage the flow of traffic through the API Gateway 102. The system 100 comprises a plurality of interconnected layers, including a Feature Processing Layer 110, a Predictive Analysis Layer 120, a Decision Control Layer 130, and a Feedback Optimization Layer 140. These layers may be implemented as software modules, microservices, or a combination thereof, executed by one or more processors on one or more computing devices.

The API Gateway 102 is a standard or custom-built gateway responsible for request routing, composition, and protocol translation. In various embodiments, the API Gateway 102 can be an open-source solution like NGINX or Kong, a cloud-provider service such as Amazon API Gateway or Google Cloud API Gateway, or a proprietary enterprise gateway. The API Gateway 102 is instrumented to not only execute control commands from the system 100 but also to export detailed operational data that serves as an input to the system.

The Backend Services 106 represent the collection of microservices, serverless functions, or monolithic applications that fulfill the business logic of the API requests. These services are the resources that the system 100 aims to protect from overload, misuse, and failure.

The Data Sources 104 provide the raw information upon which the system's intelligence is built. These sources are diverse and can include, without limitation, API Gateway 102 access logs (containing information like source IP, requested endpoint, HTTP method, user agent, response code, and latency), infrastructure metrics from the Backend Services 106 (e.g., CPU utilization, memory consumption, disk I/O, network bandwidth), application performance monitoring (APM) data (e.g., transaction traces, error rates), and external threat intelligence feeds (e.g., lists of known malicious IP addresses or botnet command-and-control servers). In one embodiment, data is streamed in real-time from these sources using a message bus like Apache Kafka. In another embodiment, data may be collected in batches from log aggregators like an ELK stack (Elasticsearch, Logstash, Kibana) or Splunk.

The Feature Processing Layer 110 is the first stage of the intelligence pipeline. Its primary function is to transform the heterogeneous, raw data from Data Sources 104 into a structured, numerical format, known as features, that can be readily processed by machine learning models. This layer includes a Data Ingestion Module 112 and a Feature Extraction Module 114.

The Data Ingestion Module 112 is responsible for connecting to the various Data Sources 104 and collecting the data. It handles different data formats (e.g., JSON logs, Prometheus metrics) and protocols. It may perform initial cleaning, such as removing corrupted records or handling missing values.

The Feature Extraction Module 114 performs the core task of feature engineering. It computes a rich set of features from the ingested data. For example, from raw request logs, it can derive time-series features such as request rate per second for a specific endpoint, average response latency over a rolling 5-minute window, or the rate of 5xx server errors. It can also extract categorical features, such as the geographic region of the source IP address or the type of client (e.g., mobile app vs. web browser) derived from the user agent string. Other exemplary features include payload size, request header complexity, JWT (JSON Web Token) claims, and the sequence of endpoints called by a single user in a session. The module normalizes numerical features (e.g., using Min-Max scaling or Z-score standardization) to ensure they are on a comparable scale, which is crucial for the performance of many machine learning algorithms.

The Predictive Analysis Layer 120 is the brain of the system 100. It receives the engineered features from the Feature Processing Layer 110 and uses sophisticated models to make predictions. This layer can house a variety of models tailored to different tasks. As shown in FIG. 1, it may include a Forecasting Model 122 and an Anomaly Detection Model 124.

The Forecasting Model 122 is responsible for predicting future traffic patterns. In one embodiment, this could be a time-series forecasting model, such as ARIMA (Autoregressive Integrated Moving Average), Prophet, or a more complex deep learning model like a Long Short-Term Memory (LSTM) network. This model could predict, for example, the expected request volume for the next hour, enabling the system to proactively scale backend resources.

The Anomaly Detection Model 124 is designed to identify unusual or suspicious patterns in the traffic that deviate from established baselines. This is critical for security and reliability. For instance, a sudden, sharp increase in requests from a single IP address to a login endpoint could signify a credential stuffing attack. An unusually large request payload for a specific endpoint could indicate an attempt to exploit a buffer overflow vulnerability. In one embodiment, an unsupervised learning model like Isolation Forest or a One-Class Support Vector Machine (SVM) can be used to learn the characteristics of normal traffic and flag any deviations as anomalous. In another embodiment, a supervised classification model, such as a Gradient Boosting Machine or a Random Forest, could be trained on labeled data of past attacks (e.g., DDoS, SQL injection) to identify specific threat types.

The outputs of the Predictive Analysis Layer 120 are not raw data but actionable insights—for example, "High probability (95%) of a 300% traffic surge on the /checkout API in the next 15 minutes," or "Anomalous activity detected from IP block X.X.X.X, characteristic of a DDoS attack."

The Decision Control Layer 130 acts on the predictions and insights generated by the layer 120. It translates high-level predictions into low-level, concrete control commands that the API Gateway 102 can execute. This layer comprises a Policy Engine 132 and a Command Generation Module 134.

The Policy Engine 132 contains the logic for making decisions. This logic can be implemented in several ways. In a simpler embodiment, it could be a set of human-defined rules, such as "IF predicted traffic > 1000 RPS AND backend CPU > 80%, THEN initiate throttling for low-priority clients." In a more advanced embodiment, the Policy Engine 132 could employ an optimization algorithm that seeks to maximize a goal (e.g., user satisfaction) subject to constraints (e.g., backend service capacity). In a highly sophisticated embodiment, the Policy Engine 132 may be a reinforcement learning (RL) agent. The RL agent learns the optimal control policy over time through trial and error, receiving rewards for actions that lead to good outcomes (e.g., preventing an outage) and penalties for actions that lead to bad outcomes (e.g., unnecessarily blocking legitimate users).

The Command Generation Module 134 takes the decision from the Policy Engine 132 and formulates the specific command for the API Gateway 102. For example, if the decision is to rate-limit a user, this module will generate the API call or configuration update to instruct the API Gateway 102 to enforce a new, lower request limit for that specific user's API key. Other examples of commands include: dynamically routing a percentage of traffic to a newly scaled-up service instance, adding a malicious IP to a denylist, or instructing the gateway to serve a specific response from its cache instead of hitting the backend service.

The Feedback Optimization Layer 140 ensures that the system 100 is a closed-loop, self-improving system. It is responsible for evaluating the effectiveness of the system's actions and using that information to refine its internal models and logic. This layer includes a Performance Monitor 142 and a Model Retraining Module 144.

The Performance Monitor 142 continuously observes the state of the system after a control action has been taken. It collects "ground truth" data from the API Gateway 102 and other Data Sources 104. For example, if the system predicted a traffic surge and applied throttling, the monitor would track the actual resulting latency and error rates of the backend services. It compares the actual outcome with the predicted outcome to measure the accuracy of the prediction and the efficacy of the control action.

The Model Retraining Module 144 uses the feedback data from the Performance Monitor 142 to update the models in the Predictive Analysis Layer 120. For instance, the data about actual traffic patterns is used to retrain the Forecasting Model 122, improving its future accuracy. Data on correctly or incorrectly identified anomalies is used to retrain the Anomaly Detection Model 124, reducing false positives and false negatives. This retraining can occur periodically in batches or, in more advanced systems, via online learning, where the models are updated incrementally with each new piece of feedback data. This continuous feedback loop allows the system 100 to adapt to concept drift—the natural evolution of traffic patterns over time—and to learn from its mistakes, becoming progressively more intelligent and effective.

Referring now to FIG. 2, a flowchart illustrates a method 200 for intelligent API gateway traffic control. The process begins at step 202, where the system continuously collects real-time and historical data from various sources, such as gateway logs, infrastructure metrics, and application performance data. This step is primarily performed by the Data Ingestion Module 112 of the Feature Processing Layer 110.

At step 204, the collected raw data is processed and transformed into a structured set of features. This involves cleaning the data, handling missing values, and engineering high-level features like rolling averages of request rates or error percentages. This step corresponds to the function of the Feature Extraction Module 114.

At step 206, the extracted features are fed into the machine learning models of the Predictive Analysis Layer 120. The models analyze the features to predict future traffic states, such as impending traffic surges, or to identify anomalies that may represent security threats or operational problems.

At decision block 208, the system evaluates the output of the predictive models. It determines whether any significant anomalies or potential issues have been predicted. If no issues are predicted, the method proceeds to step 212, where normal operation is maintained, and incoming API requests are forwarded by the API Gateway 102 according to its standard configuration. The process then effectively ends for that particular analysis cycle at step 218, while the data collection continues in the background.

If, however, an issue is predicted at step 208, the method proceeds to step 210. Here, the Decision Control Layer 130 generates a proactive control decision designed to mitigate the predicted issue. For example, if a DDoS attack is predicted, the decision might be to block the offending IP addresses and activate a higher level of request scrutiny. If a service overload is predicted, the decision might be to apply rate limits to non-critical API clients.

At step 214, the control decision is translated into a specific command and applied to the API Gateway 102. The gateway immediately alters its behavior to enforce the new policy, for example, by dropping requests from a blocked IP or returning a "429 Too Many Requests" error to a rate-limited client.

At step 216, the Feedback Optimization Layer 140 begins to monitor the outcome of the control decision. It observes metrics like backend service latency, error rates, and user-reported issues to determine if the action was effective. This outcome data is collected and formatted as feedback. This feedback data is then looped back to step 204 (and by extension, step 206), where it is used as a new input to refine future feature extraction and, more importantly, to retrain the predictive models, thus completing the adaptive learning loop. This ensures the system's performance improves over time.

CLAIMS

What is claimed is:

A system for intelligent traffic control of an Application Programming Interface (API) gateway, the system comprising:

a processor; and a memory communicatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the system to implement:

a feature processing layer configured to receive raw data related to API traffic from one or more data sources and transform the raw data into a plurality of structured features;

a predictive analysis layer communicatively coupled to the feature processing layer, the predictive analysis layer configured to receive the plurality of structured features and, using at least one machine learning model, generate a prediction related to a future state of the API traffic;

a decision control layer communicatively coupled to the predictive analysis layer, the decision control layer configured to receive the prediction and generate a control command for the API gateway based on the prediction; and

a feedback optimization layer configured to monitor an outcome resulting from an execution of the control command by the API gateway and update the at least one machine learning model based on the outcome.

The system of claim 1, wherein the one or more data sources include at least one of API gateway access logs, server performance metrics, network latency data, application performance monitoring data, or external threat intelligence feeds.

The system of claim 1, wherein the plurality of structured features includes at least one of a request rate, a response latency, a payload size, a server error rate, a source IP address, or a user agent identifier.

The system of claim 1, wherein the at least one machine learning model is a time-series forecasting model configured to predict a future traffic volume.

The system of claim 4, wherein the time-series forecasting model is a Long Short-Term Memory (LSTM) network.

The system of claim 1, wherein the at least one machine learning model is an anomaly detection model configured to identify an anomalous request pattern in the API traffic.

The system of claim 6, wherein the anomaly detection model is an Isolation Forest model or a One-Class Support Vector Machine (SVM) model.

The system of claim 1, wherein the control command comprises an instruction for the API gateway to perform at least one of: dynamically adjusting a rate limit, intelligently routing traffic to a specified backend service, applying an adaptive caching policy, or blocking requests from a specified source.

The system of claim 1, wherein the decision control layer comprises a reinforcement learning agent configured to learn an optimal policy for generating the control command over time.

The system of claim 1, wherein the feedback optimization layer is configured to compare the prediction with the outcome to generate a performance metric, and wherein the at least one machine learning model is updated using the performance metric.

A computer-implemented method for intelligent traffic control of an Application Programming Interface (API) gateway, the method comprising:

receiving, by a processor, raw data related to API traffic from one or more data sources;

transforming, by the processor, the raw data into a plurality of structured features;

generating, by the processor using at least one machine learning model, a prediction related to a future state of the API traffic based on the plurality of structured features;

generating, by the processor, a control command for the API gateway based on the prediction;

transmitting the control command to the API gateway for execution;

monitoring, by the processor, an outcome resulting from the execution of the control command; and

updating, by the processor, the at least one machine learning model based on the outcome.

The method of claim 11, wherein the raw data comprises API gateway access logs and server performance metrics.

The method of claim 11, wherein generating the prediction comprises forecasting a future traffic volume using a time-series forecasting model.

The method of claim 11, wherein generating the prediction comprises identifying an anomalous request pattern indicative of a security threat using an anomaly detection model.

The method of claim 11, wherein the control command is an instruction to dynamically adjust a rate limit for a subset of the API traffic.

The method of claim 11, wherein the control command is an instruction to intelligently reroute a portion of the API traffic from a first backend service to a second backend service.

The method of claim 11, wherein updating the at least one machine learning model comprises retraining the model using the outcome as a new data point.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to perform a method for intelligent traffic control of an Application Programming Interface (API) gateway, the method comprising:

processing raw data related to API traffic into a plurality of features;

analyzing the plurality of features with a predictive model to generate a prediction about a future traffic condition;

generating a control policy for the API gateway based on the prediction;

monitoring an actual traffic condition after the control policy is applied; and

refining the predictive model based on a comparison between the prediction and the actual traffic condition.

The non-transitory computer-readable medium of claim 18, wherein the prediction identifies a potential Distributed Denial of Service (DDoS) attack, and the control policy includes instructions to block traffic from identified malicious sources.

The non-transitory computer-readable medium of claim 18, wherein the prediction identifies a future traffic surge, and the control policy includes instructions to proactively apply rate limiting to low-priority API clients.

ABSTRACT OF THE DISCLOSURE

A system and method for intelligent traffic control at an Application Programming Interface (API) gateway are disclosed. The system employs a layered architecture comprising a feature processing layer, a predictive analysis layer, a decision control layer, and a feedback optimization layer. The feature processing layer ingests raw data from sources like gateway logs and server metrics and transforms it into structured features. The predictive analysis layer uses machine learning models, such as time-series forecasting or anomaly detection models, to analyze these features and predict future traffic states, including potential surges or security threats. The decision control layer receives these predictions and generates proactive control commands, such as dynamic rate limiting, intelligent routing, or security blocking, which are executed by the API gateway. A feedback optimization layer monitors the outcome of these commands and continuously updates the machine learning models, creating a self-improving system that enhances API reliability, security, and performance.