Machine Operation Recognition Pipeline for Context-Rich Threat Detection
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Solution Overview
Problem
Modern network monitoring systems face challenges in interpreting vast volumes of network sensor events, which lack contextual information about user or machine activities, leading to resource wastage and potential gaps in detecting novel attack methods.
Innovation Solution
Implementing a network data interpretation pipeline that classifies network sensor events into machine operations and activities, using recognition engines to organize data into human-friendly structures, reducing data volume, and enabling continuous learning and threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network sensors continuously monitor and upload all network traffic metadata to a centralized platform, then comprehensive network security monitoring is achieved, but resource usage increases and security concerns are raised
Solution Approach 1:
The patent segments the network monitoring system into distributed network sensors that collect metadata locally and a centralized platform that receives processed information. Each sensor operates independently to monitor its local network segment, uploading only relevant security-related data rather than all traffic metadata, thereby reducing resource consumption while maintaining comprehensive monitoring coverage
Solution Approach 2:
The patent extracts only the essential security-related features and anomalies from the raw network traffic metadata at the sensor level before transmission. By filtering and selecting only critical information for upload to the centralized platform, the system achieves comprehensive security monitoring with significantly reduced data transmission and processing resources
2Loss of information
If network sensors upload all raw sensor events to the centralized platform, then complete network traffic information is available, but data upload volume becomes excessive and processing becomes inefficient
Solution Approach 1:
The patent performs preliminary processing of network sensor events at the distributed sensor level before upload. Network sensors pre-analyze traffic patterns, identify anomalies, and aggregate relevant information locally, so that only processed and condensed security-related data is transmitted to the centralized platform, reducing upload volume while preserving essential information
Solution Approach 2:
The patent transforms raw network traffic metadata from a high-dimensional, voluminous format into a lower-dimensional representation by extracting key security features and aggregating traffic patterns. This dimensional reduction maintains the essential security information while dramatically reducing the quantity of data that needs to be uploaded and processed
3Measurement precision
If network monitoring systems analyze only known patterns of suspicious behavior, then detection of known threats is effective, but novel attack patterns are missed
Solution Approach 1:
The patent implements feedback mechanisms where the centralized platform continuously analyzes uploaded network sensor data, identifies novel attack patterns, and updates detection models and rules that are then distributed back to network sensors. This closed-loop system enables the network to adapt to new threats while maintaining precise detection of known attack patterns
Solution Approach 2:
The patent employs dynamic detection models that can adapt their parameters and thresholds based on evolving network traffic patterns and emerging threats. The system transitions from static signature-based detection to dynamic behavior-based detection, allowing it to effectively identify both known threats and novel attack patterns by learning from continuous data analysis
4Measurement precision
If considerable technical knowledge and labor are invested to sift through sensor events, then accurate reconstruction of network activities is achieved, but time and resource consumption increase
Solution Approach 1:
The patent introduces an intermediary layer of automated analysis between raw sensor events and human analysts. The system automatically correlates sensor events, reconstructs network activities, and presents processed information in an intuitive format, eliminating the need for analysts to manually sift through raw data while maintaining accurate activity reconstruction
Data Source
AI summary
Systems and methods are disclosed to implement a network data interpretation pipeline to recognize machine operations (MOs) and machine activities (MAs) from network traffic data observed in a monitored network. In embodiments, a MO recognition engine is implemented in the network to recognize MOs from network sensor events (NSEs) based on defined recognition patterns. The MOs and any unrecognized NSEs are uploaded to a network monitoring system, where they are further analyzed by a MA recognition engine to recognize higher-level machine activities performed by machines. The NSEs, MOs, and MAs are used by the network monitoring system to implement a variety of security threat detection processes. Advantageously, the pipeline may be used to add rich contextual information about the raw network data to facilitate security threat detection processes. Additionally, the MOs and MAs can be used to present the raw network data in a variety of intuitive user interfaces.