Honeypot Attack Pattern Modeling via Metadata Clustering
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Solution Overview
Problem
Existing methods for analyzing network attack patterns in honeypot systems require analyzing large volumes of network traffic data, leading to high computational power and storage needs, and often involve manual scanning or supervised classification methods that are time-consuming and resource-intensive.
Innovation Solution
A method for modeling cyber-attack patterns in honeypot systems that focuses on analyzing a subset of metadata, using feature extraction in two phases, and employing clustering algorithms like k-means and EM-Clustering to identify patterns without requiring the entire network traffic trace, allowing for efficient data analysis and automated pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete network traffic data is analyzed to determine attack patterns, then measurement precision is improved, but use of energy and computational power increase significantly
Solution Approach 1:
The patent extracts and analyzes only the metadata portion of network traffic data rather than processing complete traffic traces. The metadata contains essential attack pattern information (source IP, destination IP, ports, protocols, timestamps) while being significantly smaller in volume, thus reducing computational energy requirements while maintaining detection accuracy
Solution Approach 2:
The patent segments the attack detection process into two phases: (1) collecting metadata from network traffic, and (2) analyzing the metadata to identify attack patterns. This segmentation allows the system to focus computational resources on analyzing structured metadata rather than processing raw traffic data, reducing overall energy consumption
2Reliability
If complete network traffic data is stored for analysis, then reliability of attack pattern modeling is improved, but loss of substance increases due to storage requirements
Solution Approach 1:
The patent extracts only the necessary metadata fields from complete network traffic data for storage and analysis. By storing only essential information (metadata) rather than complete traffic traces, the system maintains sufficient data reliability for attack pattern modeling while dramatically reducing storage space requirements
3Measurement precision
If manual scanning is used to analyze attack data groups, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces manual scanning with automated computational analysis of metadata. The system uses algorithms to automatically process and analyze metadata, identifying attack patterns without human intervention. This substitution maintains analysis precision while eliminating the time loss associated with manual scanning
4Manufacturing precision
If supervised classification methods are used to categorize attacks, then manufacturing precision is improved, but loss of time increases due to tagging requirements
Solution Approach 1:
The patent performs preliminary organization of attack data by grouping metadata into structured data groups based on common characteristics before analysis. This preliminary action automates the categorization process, maintaining classification precision while reducing the time required compared to manual supervised tagging of each attack
Data Source
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AI summary
A monitored honeypot system, comprising sensors for monitoring data being communicated between an attacker and said honeypot; a hardware processor adapted to analyze a portion of said communicated data by defining a set of attack features; obtaining a dataset comprising monitored cyber-attack metadata; filtering said dataset to a desired contextual subset of cyber-attacks; defining attack sessions from said contextual subset; extracting values for each feature, respectively, from each said attack sessions; generating from said features values attack profiles, each profile relating to a set of attacks; clustering the attack profiles based on features of said profiles; enriching the centroid representation of said attack profiles; outlining important features which distinguish between said pattern clusters; calculating a closeness similarity between said pattern-clusters; generating a model of cyber-attack patterns, based the preceding steps; and a memory for storing the form model.