Network Security Correlation Engine for Threat Detection
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Solution Overview
Problem
Conventional network security systems require complex integrations, significant resources, and frequent maintenance, making them inefficient and costly for threat management, and they often fail to detect emerging threats due to reliance on discrete signature-based solutions.
Innovation Solution
A system that includes detection modules for collecting binary network packet data, a correlation module for analyzing behavioral patterns, and a remote database for storing and displaying potential security threats in real-time, allowing for prioritized alerts and updates without the need for correlation rule development or third-party integrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security products are used, then specific security aspects can be addressed, but complex integrations and significant resources are required for threat management
Solution Approach 1:
The patent combines multiple security functions (intrusion detection, vulnerability management, malware analysis, threat intelligence) into a single integrated platform. The system merges data from various sources including network traffic, system logs, and threat intelligence feeds into a unified correlation engine that processes all security events centrally, eliminating the need for complex integrations between separate security products.
Solution Approach 2:
The security system is designed as a universal platform that performs multiple security functions simultaneously. The correlation engine handles diverse data types (network packets, logs, alerts) and executes various security analyses (intrusion detection, vulnerability assessment, behavior analysis) through a single system, replacing the need for multiple specialized products and their complex integrations.
2Reliability
If conventional software applications are used, then security functions can be provided, but significant time and resources are required for rule development and maintenance
Solution Approach 1:
The system employs machine learning algorithms and behavioral analysis that automatically adapt to network patterns without requiring manual rule development. The correlation engine learns normal network behavior and automatically detects anomalies, while the malware analysis module uses sandboxing and heuristic analysis to identify threats without pre-programmed signatures. This self-service capability eliminates the need for continuous manual rule updates and maintenance.
Solution Approach 2:
The system dynamically adjusts security parameters and detection thresholds based on real-time network conditions and learned patterns. Instead of static rules that require manual updates, the correlation engine modifies its analysis parameters automatically based on network traffic characteristics, enabling the system to adapt to changing threat landscapes without time-consuming rule reconfiguration.
3Reliability
If discrete signature-based solutions are used, then known threats can be detected, but emerging threats cannot be detected
Solution Approach 1:
The system transitions from static signature-based detection to dynamic behavioral analysis. The correlation engine continuously monitors network behavior patterns and detects anomalies that indicate emerging threats, even without predefined signatures. The malware analysis module uses dynamic sandboxing to observe how unknown files behave in controlled environments, enabling detection of previously unknown malware variants and attack techniques.
Solution Approach 2:
The system incorporates feedback loops where detected anomalies and emerging threat patterns are fed back into the correlation engine and machine learning models. This feedback mechanism enables the system to continuously improve its detection capabilities, learning from new threat data and updating its behavioral baselines to detect emerging threats more effectively over time.
Data Source
AI summary
The present disclosure generally provides systems and methods of network security and threat management. An exemplary system includes detection and prevention modules (DPM) designed specifically to collect and transmit suspicious binary network packet data. The collected network packets are sent to a behavioral correlation module to perform automatic behavioral correlation: (1) within each DPM, (2) across all DPMs installed on a network, and (3) across all DPMs installed on all networks. The results of the behavioral correlation are sent to a security dashboard module (SDM), which generally acts as a fully integrated Security Event Management system and collects, correlates, and prioritizes global network alerts, local network alerts, posted vendor alerts, and detected network vulnerabilities with enterprise assets. The SDM could display the results in a user-friendly graphical user interface and has the ability to perform geographic mapping of externally generated threats.