Network Traffic Metadata Indexing for Malicious Activity Detection
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Solution Overview
Problem
Current systems lack effective methods to detect and analyze malicious activity within computer networks, providing insufficient evidence for infection identification and understanding the severity of threats, and struggle to discover precursors and implications of such activity.
Innovation Solution
The system monitors network traffic, captures and indexes metadata, compares it to simulated data to identify malicious patterns, and re-examines historical data to understand infection lifecycles and precursors, using deep packet inspection and traffic analysis to determine the probability of maliciousness and automation in network activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical network data is captured and re-examined to discover precursors and infection lifecycles, then detection accuracy and understanding of malicious activity is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary capture and indexing of network data as it flows through the network, storing metadata and packet information in advance. This allows historical re-examination without requiring complex real-time processing during threat analysis, as the data preparation work was done beforehand during normal network operation.
Solution Approach 2:
The system creates copies of network traffic data and metadata for storage and analysis, rather than processing the original live traffic. Multiple copies are maintained at different stages (captured traffic, indexed metadata, re-examined historical data) to enable various analysis operations without interfering with normal network operations or requiring complex real-time processing.
2Loss of information
If deep packet inspection and traffic analysis are performed to identify malicious patterns, then ability to understand infection severity and threats is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing and metadata extraction from network packets as they pass through the network, creating structured data representations in advance. This preliminary processing organizes the data so that subsequent deep inspection and pattern matching operations can be performed more efficiently on pre-processed data rather than raw packets.
Solution Approach 2:
The system extracts and separates metadata from full packet data, indexing only the essential metadata fields for quick comparison and analysis. Full deep packet inspection is performed selectively on suspicious traffic or historical re-examination, rather than continuously on all traffic, reducing overall processing time while maintaining detection capability.
3Reliability
If network data is captured and indexed for future analysis, then ability to detect precursors and analyze infection lifecycles is improved, but data storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential metadata from network packets, such as source/destination addresses, ports, protocols, and timing information, rather than storing complete packet captures. This selective extraction maintains the ability to detect precursors and analyze infection patterns while significantly reducing storage requirements compared to full packet archiving.
Solution Approach 2:
The system performs preliminary filtering and selection of which network data to capture and index based on predefined criteria and threat indicators. Only data that meets certain thresholds or matches known malicious patterns is captured and stored for historical analysis, reducing storage requirements while maintaining detection reliability for actual threats.
Data Source
AI summary
Systems and methods may use historical analysis to identify malicious activity. A discovery/recovery system may comprise a processor in communication with a network and in communication with a database. The discovery/recovery system may gather filtered historical network data associated with an asset associated with the network. The discovery/recovery system may analyze the filtered historical network data to determine whether a subset of the filtered historical network data is associated with a malware infection of the asset.