Network Security Threat Detection via Metadata Correlation
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Solution Overview
Problem
Current methods for detecting malware callback, command and control channels, and data exfiltration in networks are inefficient, often requiring manual analysis after a breach has occurred and are challenging to implement in dynamic and encrypted environments, especially in virtual and cloud-based networks.
Innovation Solution
A system that receives and analyzes real-time network data to identify security threats by correlating network infrastructure, connection topology, and device data with threat intelligence, allowing for real-time detection and remediation of malicious activities without the need for deep packet inspection or decryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of network logs is performed after a breach occurs, then security personnel can identify malicious activities, but the detection time is delayed and cannot prevent ongoing breaches
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing network data in real-time, establishing baselines of normal network behavior before breaches occur. This enables the system to detect anomalies as they happen rather than after the fact, preventing ongoing breaches while maintaining high detection accuracy through pre-established behavioral patterns
Solution Approach 2:
The patent replaces manual mechanical analysis of network logs with automated electronic systems that continuously monitor and analyze network traffic. This substitution eliminates the time delay inherent in manual review while maintaining or improving detection accuracy through consistent, rule-based analysis of network patterns
2Measurement precision
If deep packet inspection and decryption methods are used to detect encrypted malicious traffic, then detection capability is improved, but deployment complexity and cost increase significantly
Solution Approach 1:
The system extracts and analyzes only the essential metadata elements from network packets (source/destination IPs, ports, protocols, timing patterns) rather than performing deep inspection of encrypted packet contents. This extraction approach maintains detection capability by focusing on observable behavioral patterns while avoiding the complexity of decrypting and analyzing encrypted payloads
Solution Approach 2:
The patent introduces behavioral analysis as an intermediary method between traditional signature-based detection and direct packet inspection. Instead of attempting to decrypt or deeply inspect packets, the system uses behavioral patterns as a mediator to infer malicious activity from observable network metadata, simplifying deployment while maintaining effectiveness
3Reliability
If traditional security monitoring systems are deployed in virtual and cloud-based networks, then detection of malicious activities is possible, but implementation becomes challenging due to dynamic and encrypted environments
Solution Approach 1:
The system achieves universality by designing a platform that can operate across multiple network environments (physical, virtual, cloud-based) using the same core behavioral analysis methodology. The solution collects and analyzes network metadata in a manner that is applicable regardless of the underlying infrastructure type, making implementation straightforward while maintaining reliable detection effectiveness across diverse environments
Data Source
AI summary
An index of network data is received, the index including network infrastructure data, network connection topology data and network devices data, collected in real time. Data describing one or more cybersecurity threat sources is received. Data describing communications occurring with devices within the network is received. The data describing the one or more cybersecurity threat sources and the data describing the communications occurring with devices within the network are analyzed to identify data describing possible security threats. The data describing the possible security threats is correlated with the index of network data to identify security threats to devices within the network.