Baselining, Perceived Deviation, Anomaly Detection and Clean Room usage of AI in networking

1. Baselining in Networking Using DNNs

Definition: Baselining involves creating a model of "normal" network behavior based on metrics such as traffic volume, latency, error rates, and packet flows. This baseline acts as a reference to detect deviations.

DNN Implementation:

1. Feature Extraction: Collect features from network traffic, such as packet size, inter-arrival times, protocol type, and flow direction.

2. Model Choice:

Autoencoders: Train the network to reconstruct input data. During inference, deviations (errors in reconstruction) indicate anomalous behavior.

Recurrent Neural Networks (RNNs): Capture temporal patterns in traffic over time.

Transformers: Use for high-dimensional network data where long-range dependencies are crucial (e.g., correlating events across multiple layers).

3. Steps:

Data Collection: Gather data from network interfaces. Use tools like NetFlow, IPFIX, or packet captures.

Normalization: Scale features to ensure uniform learning.

Training: Train the DNN exclusively on normal traffic data for a set period (e.g., one week of normal operation).

Baseline Creation: Use the trained model as the baseline, mapping traffic to low-dimensional embeddings that represent "normal" traffic behavior.

4. Outcome: When traffic characteristics significantly deviate from this baseline, it signals unusual or suspicious activity.

2. Perceived Deviation in Networking Using DNNs

Definition: Perceived deviation quantifies how far a network's current behavior deviates from the baseline. It translates raw deviations into a score that indicates anomaly severity.

DNN Implementation:

1. Deviation Metrics:

Reconstruction error: Difference between input data and its reconstruction (Autoencoder).

Prediction error: Gap between predicted and actual values in time-series data (RNN, LSTM, GRU).

Classification confidence: Distance of a sample from learned decision boundaries (classification networks).

2. Key Techniques:

Latent Space Representation: Use DNNs to project data into a latent space. Measure deviations in this space (e.g., cosine similarity or Euclidean distance).

Dynamic Thresholds: Compute thresholds based on statistical methods (e.g., z-scores, moving averages) in real-time.

Ensemble Models: Combine multiple DNN architectures to achieve more robust deviation scoring.

3. Output: A score that reflects the level of abnormality, enabling prioritization for further investigation.

3. Anomaly Detection in Networking Using DNNs

Definition: Anomaly detection identifies events, patterns, or behaviors that differ significantly from normal operations. In networking, anomalies may include DDoS attacks, malware activity, or hardware failures.

DNN Approaches:

1.Supervised Learning:

Use labeled data where normal and anomalous events are pre-identified.

Models: Convolutional Neural Networks (CNNs), Dense Neural Networks.

Limitation: Requires extensive labeled datasets, which are rare in networking.

2.Unsupervised Learning:

Suitable for situations with limited labeled data.

Models:

Autoencoders: Learn to reconstruct normal traffic and flag anomalies as outliers.

Variational Autoencoders (VAEs): Generate probabilistic reconstructions, allowing anomaly detection based on reconstruction likelihood.

GANs: Use the discriminator to identify anomalies by detecting samples that differ from synthetic "normal" data.

3.Semi-Supervised Learning:

Combines labeled and unlabeled data. For example, train the model on known normal behavior and adapt it to identify anomalies in unlabeled data.

Workflow:

1. Data Preprocessing: Remove noise and extract relevant features (e.g., using PCA for dimensionality reduction).

2.Model Training: Train on normal traffic to capture patterns of legitimate operations.

3.Anomaly Detection: Apply the model to live traffic to identify outliers based on reconstruction/prediction errors or classification probabilities.

Evaluation Metrics:

True Positive Rate (TPR): Detects actual anomalies.

False Positive Rate (FPR): Measures incorrect anomaly detections.

Precision-Recall Curves: Analyze model effectiveness under imbalanced data conditions.

4. Clean Room in Networking Using DNNs

Definition: A clean room in networking refers to an isolated, controlled environment where network data is analyzed without external interference. It is useful for benchmarking, training, and validating models without exposing sensitive data.

DNN Application:

1.Data Anonymization: Clean rooms anonymize data (e.g., remove personally identifiable information) before feeding it to DNNs.

2.Model Training:

Train models in isolated environments using sanitized, representative datasets.

Use federated learning to enable decentralized training while maintaining data privacy.

3.Synthetic Data Generation:

GANs can simulate network traffic for testing purposes.

These models generate realistic traffic patterns, reducing dependency on sensitive data.

Steps to Implement Clean Room with DNNs:

1.Environment Setup:

Create an isolated network segment (physical or virtual) with no external connectivity.

Deploy monitoring and traffic capture tools.

2.Data Processing:

Sanitize data to remove sensitive information.

Perform feature extraction to convert raw data into usable input for the DNN.

3.Model Deployment:

Use pre-trained DNNs or train new models in the clean room.

Test model behavior against controlled scenarios (e.g., simulated attacks).

4.Validation:

Validate models in the clean room before applying them to live networks.

Advantages:

Ensures data privacy and security.

Facilitates robust testing without risking real-world operations.

Helps maintain compliance with data regulations (e.g., GDPR, CCPA).

Conclusion

Using DNNs for baselining, perceived deviation, anomaly detection, and clean room environments significantly enhances the ability to monitor and secure networks. They provide a robust, data-driven foundation for handling complex, dynamic network conditions, enabling real-time detection and mitigation of threats.