Anomaly Detection in Network Address Groups Using Ripeness Scores
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Solution Overview
Problem
Current network security systems face challenges in accurately detecting anomalous network addresses due to high false positive rates, requiring effective models of normal behavior to differentiate between normal and anomalous activities.
Innovation Solution
A method is introduced that builds a model of network addresses using tree data structures, where each node is assigned a ripeness score, and network addresses are classified as normal or anomalous based on their deviation from the model, with additional actions triggered for anomalous addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection is implemented using traditional signature-based approaches, then detection capability is improved, but false positive rate increases
Solution Approach 1:
The system performs preliminary learning of normal network behavior patterns by analyzing historical network address data before actual anomaly detection. This pre-training phase builds a baseline model of legitimate activity, allowing the system to distinguish true anomalies from normal variations, thereby reducing false positives while maintaining detection capability
Solution Approach 2:
The system continuously updates its understanding of normal behavior by incorporating new network address data into the tree structure. This feedback mechanism allows the anomaly detection model to adapt to changing network patterns over time, improving reliability by reducing false alarms while maintaining precise anomaly detection
2Measurement precision
If a detailed model of normal behavior is built to improve anomaly detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The network address space is segmented into a hierarchical tree structure where each node represents a portion of network addresses. This segmentation allows the system to build detailed models at appropriate levels of granularity without overwhelming complexity, enabling precise anomaly detection while managing model complexity through structured organization
Solution Approach 2:
The system transforms the complex problem of anomaly detection into a different dimensional space by organizing network addresses into tree structures with ripeness scores. This dimensional transformation simplifies the detection process by converting complex pattern recognition into path traversal and score comparison operations
Data Source
AI summary
A method for identifying anomalies in a group of network addresses includes building a model of the group of network addresses and identifying a network address as anomalous based on the deviation of the network address from the model. The model is built from a group of network addresses. The network addresses are input and parsed into one or more address trees. A ripeness score is maintained for each of the nodes in the address trees, based, at least in part, on the number of occurrences of the network address portion represented by the node. Nodes having respective ripeness scores within a specified range are classified as ripe nodes, and may be indicative of normal behavior, and nodes having respective ripeness scores outside the specified range of ripeness scores are classified as unripe, and may be indicative of anomalous behavior.