Network Tunneling Detection via VPN Log Correlation
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Solution Overview
Problem
Current systems fail to effectively detect and handle malicious network tunneling, which can compromise enterprise network security by allowing unauthorized data transfer and bypassing network restrictions.
Innovation Solution
The system accesses VPN logs and outbound data connection logs to identify IP addresses present in both, retrieves user and traffic data, generates a risk score based on this data, and alerts administrators if the score exceeds a threshold, potentially terminating malicious connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If VPN connections are allowed to enable remote access to enterprise network, then ease of operation is improved, but security is worsened due to potential malicious tunneling
Solution Approach 1:
The system implements continuous monitoring of network traffic patterns and VPN connection behavior, comparing actual usage against established baselines. When deviations indicating potential tunneling are detected, the system provides feedback by generating alerts and notifications to security administrators, enabling timely intervention while maintaining legitimate VPN access.
Solution Approach 2:
The patent introduces an intermediary monitoring system positioned between the VPN gateway and enterprise network resources. This intermediary component analyzes traffic flows, identifies suspicious patterns characteristic of tunneling protocols, and intercepts potentially malicious connections without disrupting legitimate VPN usage, thus mediating between security concerns and operational needs.
2Object-affected harmful factors
If network monitoring is implemented to detect malicious tunneling, then security is improved, but device complexity is worsened
Solution Approach 1:
The monitoring system performs self-configuration by automatically establishing baseline traffic patterns from historical data without requiring manual input from administrators. The system autonomously updates detection thresholds, adapts to changing network conditions, and self-diagnoses performance issues, thereby reducing operational complexity while maintaining effective tunneling detection.
Solution Approach 2:
The patent employs dynamic parameter adjustment where detection sensitivity thresholds, sampling rates, and analysis windows are automatically modified based on network conditions, time of day, and observed traffic patterns. This allows the system to maintain high detection accuracy while adapting complexity levels to match current operational requirements.
3Measurement precision
If detailed traffic analysis is performed to identify tunneling protocols, then measurement precision is improved, but loss of time is worsened due to processing overhead
Solution Approach 1:
The traffic analysis process is divided into multiple independent segments: initial packet inspection for protocol identification, intermediate deep packet inspection for suspicious pattern detection, and final correlation analysis with baseline data. Each segment processes only specific aspects of traffic, reducing overall processing time while maintaining high detection accuracy through cumulative analysis.
Solution Approach 2:
The system implements periodic sampling of network traffic at strategically selected intervals rather than continuous analysis of every packet. By analyzing representative samples and using statistical methods to infer overall traffic characteristics, the system achieves high detection accuracy with significantly reduced processing overhead compared to exhaustive inspection.
Data Source
AI summary
Various systems and methods are provided that detect malicious network tunneling. For example, VPN logs and data connection logs may be accessed. The VPN logs may list client IP addresses that have established a VPN connection with an enterprise network. The data connection logs may list client IP addresses that have requested connections external to the enterprise network and remote IP addresses to which connections are requested. The VPN logs and the data connection logs may be parsed to identify IP addresses that are present in the VPN logs as a client IP address and in the data connection logs as a remote IP address. If an IP address is so present, user data and traffic data associated with the IP address may be retrieved to generate a risk score. If the risk score exceeds a threshold, an alert to be displayed in a GUI is generated.