Network Traffic Outlier Scoring via Hierarchical Clustering
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Solution Overview
Problem
Current network monitoring solutions, particularly rule-based approaches, struggle to detect sophisticated cyber-attacks due to their diminishing effectiveness in identifying unknown zero-day threats, and face challenges in handling the vast and diverse nature of network traffic data, which includes varying types of protocols and imbalanced datasets.
Innovation Solution
A system that employs a hierarchical clustering approach to divide network traffic flows into homogenous clusters, generating separate machine learning models for each cluster to detect anomalies, and continuously updates these models to adapt to changing network conditions by calculating outlier scores for each flow, allowing for better detection of malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based approaches are used to detect suspicious network activity, then implementation is simple and fast, but detection effectiveness rapidly diminishes against sophisticated threats and zero-day attacks
Solution Approach 1:
The patent segments the network traffic analysis system into multiple specialized components: unsupervised learning models for anomaly detection, supervised learning models for classification, clustering algorithms for grouping similar flows, and rule-based systems for known threat detection. Each component handles specific aspects of traffic analysis, improving overall detection effectiveness while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent creates a composite detection system that combines multiple analytical approaches (unsupervised learning, supervised learning, clustering, and rule-based methods) into a unified framework. This composite approach leverages the strengths of each method to detect both known and unknown threats, achieving high reliability without relying on a single complex system
2Measurement precision
If machine learning models are trained on entire network traffic dataset, then comprehensive coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the large network traffic dataset into multiple clusters based on flow characteristics using clustering algorithms. Each cluster represents a subset of traffic with similar properties, allowing the system to process and analyze smaller, more manageable groups rather than the entire dataset at once, thereby reducing processing time while maintaining detection accuracy
Solution Approach 2:
The patent performs preliminary clustering and grouping of network flows before applying machine learning models. By pre-organizing the data into homogeneous clusters, the system prepares the dataset in advance to enable faster and more efficient anomaly detection during actual analysis, reducing real-time processing requirements
3Measurement precision
If separate machine learning models are generated for each cluster, then detection accuracy for specific traffic types improves, but model complexity and maintenance burden increase
Solution Approach 1:
The patent generates separate machine learning models for each traffic cluster, where each model is specialized for detecting anomalies in its specific cluster type. This segmentation allows each model to achieve high precision for its designated traffic pattern while the overall system manages complexity through automated model generation and clustering, reducing manual intervention requirements
Data Source
AI summary
In some examples, a system for decorating network traffic flows with outlier scores includes a processor and a memory device to store traffic flows received from a network. The processor is configured to receive a set of traffic flows from the memory device and generate a tree model to split the traffic flows into clusters of traffic flows. Each cluster corresponds with a leaf of the tree model. The processor is further configured to generate machine learning models for each of the clusters of traffic flows separately. For a new traffic flow, the processor is configured to identify a specific one of the machine learning models that corresponds with the new traffic flow, compute an outlier score for the new traffic flow using the identified specific one of the machine learning models, and decorate the new traffic flow with the outlier score.