Network Event Pipeline Normalization and IoC Decoration
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Solution Overview
Problem
Existing network security systems face challenges in efficiently analyzing and processing large volumes of network events due to variations in data types, structures, and fluctuations in network activity, which limits their ability to detect and mitigate security threats in real-time.
Innovation Solution
The implementation of an extensible framework for pipelines that normalize and decorate network events at the time of ingestion, using Indicators of Compromise (IoCs) to enhance analysis efficiency and scalability, allowing for the efficient collection, storage, and analysis of network events, and dynamically scaling to accommodate changes in network activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network security systems process and analyze all network events in real-time, then security threat detection capability is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by normalizing and decorating network events at the time of ingestion, before analysis. Events are standardized and enriched with contextual information (IoCs, geographic data, historical behavior) during the data collection phase, so that subsequent security analysis can proceed efficiently without requiring intensive real-time computation for data preparation and enrichment.
2Measurement precision
If the system ingests and stores all network events with full annotation, then analysis accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system segments the data processing workflow into distinct pipeline stages: event normalization, decoration with contextual information, and storage. Each stage handles specific tasks independently, allowing for modular processing that maintains high analysis accuracy through comprehensive annotation while managing complexity through structured, step-by-step data transformation.
Solution Approach 2:
The event normalization and decoration pipeline serves multiple functions simultaneously: it standardizes diverse event formats into a common schema, enriches events with contextual information from multiple sources (IoCs, geographic data, historical behavior), and prepares data for efficient storage and retrieval. This multi-functional approach improves analysis accuracy without proportionally increasing system complexity.
3Productivity
If the system scales up to handle fluctuating network traffic volumes, then real-time monitoring capability is improved, but infrastructure resource requirements increase
Solution Approach 1:
The system implements dynamic scaling capabilities through its pipeline architecture, which can adjust processing capacity in response to fluctuating network traffic volumes. The event normalization and decoration pipeline can be scaled up or down based on current workload demands, allowing the system to maintain real-time monitoring capability during high-traffic periods while conserving infrastructure resources during lower-activity periods.
Data Source
AI summary
This document generally describes computer systems, processes, program products, and devices for the rapid and automated collection, storage, and analysis of network events to provide improved and enhanced security analysis. The system can include an extensible framework for pipelines to process, normalize, and decorate network events created in response to network activity, which can permit the system to readily scale up and down to ingest large volumes and variations in network activity. For example, pipeline can match data in the network events with stored Indicators of Compromise (IoCs) and decorate the network events with the IoCs before the network events are stored and subsequently analyzed.