Anomaly Detection in Spring Boot Gateway with AI and DL4J: Unsupervised Learning Approach

In this article, we’ll focus on using unsupervised learning with DL4J to detect anomalies in data traffic passing through your Spring Boot Gateway. This is especially useful when you don’t have labeled data on what constitutes “normal” vs. “anomalous” traffic.

Potential Features for Anomaly Detection in API Gateway Traffic The features you choose to extract will depend on the specific nature of your API and the types of anomalies you’re trying to detect. Here’s a list of potential features to consider:

Request-Related Features

• Request Frequency:
  • Requests per second (RPS)
  • Requests per minute (RPM)
  • Requests per hour (RPH)
  • Requests per client/IP address
• Payload Size:
  • Request payload size (bytes)
  • Response payload size (bytes)
• Request Method:
  • GET, POST, PUT, DELETE, etc. (categorical or one-hot encoded)
• Request Path:
  • Specific endpoint targeted (string or encoded)
• Request Headers:
  • User-Agent (string or encoded)
  • Referer (string or encoded)
  • Content-Type (string or encoded)

Response-Related Features

• Response Time:
  • Time to first byte (TTFB)
  • Total response time
• Status Codes:
  • Success (2xx)
  • Client errors (4xx)
  • Server errors (5xx)

Client-Related Features

• IP Address:
  • Geolocation (country, region)
  • IP reputation (blacklist/whitelist)
• User Agent:
  • Browser type
  • Operating system

Additional Features

• Time-Based Features:
  • Time of day
  • Day of week
  • Month of year
• Error Rates:
  • Percentage of error responses
• Session-Based Features:
  • Number of requests in a session
  • Session duration

Feature Engineering

You can also create new features by combining or transforming existing ones. For example:

• Average Request Size per Client: Calculate the average payload size of requests from a particular client.
• Peak Request Times: Identify the times of day when request frequency is highest.
• Error Rate per Endpoint: Calculate the error rate for specific API endpoints.

Remember:

• Start with a smaller set of features and gradually add more as needed.
• Experiment with different feature combinations to see which ones work best for your specific anomaly detection task.
• Consider using dimensionality reduction techniques if you have a large number of features.

Feel free to ask if you’d like guidance on selecting or engineering specific features for your use case!

Feature Extraction: The Key to Effective Anomaly Detection

Before diving into unsupervised learning, it’s crucial to understand feature extraction. Features are the measurable characteristics of your data that the model will use to learn patterns. In the context of API gateway traffic, relevant features might include:

• Request Frequency: How often a particular client or IP address is making requests.
• Payload Size: The size of the data being sent in the request or received in the response.
• Response Time: How long it takes for your backend services to process a request.
• Error Rates: The frequency of error responses (e.g., 500 Internal Server Errors).
• Request Method: Whether it’s a GET, POST, PUT, DELETE, etc.
• Request Path: The specific endpoint being targeted.

Extracting Features in Spring Cloud Gateway

You can extract these features using a custom GlobalFilter in Spring Cloud Gateway. Here’s an example snippet:

@Component
public class FeatureExtractionFilter implements GlobalFilter {

    @Override
    public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {
        // ... (Access request/response objects to extract features)

        Map<String, Object> features = new HashMap<>();
        features.put("requestFrequency", /* ... calculate ... */);
        features.put("payloadSize", /* ... calculate ... */);
        // ... (Extract other features)

        exchange.getAttributes().put("extractedFeatures", features);
        return chain.filter(exchange);
    }
}

Unsupervised Anomaly Detection with DL4J (with Feature Extraction)

1. Choose Your Unsupervised DL4J Model: Autoencoders or Self-Organizing Maps (SOMs).

2. Preprocess and Transform Features: Normalize or standardize your extracted features to ensure they have similar scales. This is important for many machine learning algorithms, including those in DL4J.

3. Train the Model on Normal Data with Extracted Features: Feed the preprocessed features from normal traffic data to your chosen DL4J model.

4. Integrate with Spring Cloud Gateway Filter:

   • The filter first calls the FeatureExtractionFilter to extract features.
   • Then it preprocesses the features and feeds them to the trained DL4J model.
   • Get a prediction (reconstruction error or distance to cluster center) and compare it to your anomaly threshold.

Example Code Snippet (Autoencoder)

@Component
public class AnomalyDetectionFilter implements GlobalFilter {

    // ... (Autowire FeatureExtractionFilter and the loaded DL4J autoencoder model)

    @Override
    public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {

        // ... (Call FeatureExtractionFilter to get extracted features)
        Map<String, Object> features = exchange.getAttribute("extractedFeatures");

        // ... (Preprocess features)
        INDArray input = Nd4j.create(preprocessedFeatures);

        // ... (rest of the filter logic for model prediction and anomaly handling)

        return chain.filter(exchange);
    }
}

Key Considerations:

• Feature Selection: Choose features that are most likely to distinguish normal from anomalous traffic.
• Feature Engineering: Create new features by combining or transforming existing ones to capture more complex patterns.
• Dimensionality Reduction: If you have a large number of features, consider techniques like Principal Component Analysis (PCA) to reduce the dimensionality while preserving important information.

Let me know if you’d like a deeper dive into any of these aspects, such as specific feature engineering techniques or dimensionality reduction with DL4J!