Neural Network VPN Session Anomaly Detection
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Solution Overview
Problem
Existing systems lack effective methods to detect and respond to anomalous virtual private network (VPN) sessions, which can indicate malicious activities, thereby compromising network security.
Innovation Solution
A computerized security system using machine learning, specifically a neural network, continuously monitors VPN sessions, gathers data on their characteristics, and determines the likelihood of anomalous activity, enabling automatic control or user notification to terminate suspicious sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional VPN monitoring methods are used, then system simplicity is maintained, but security detection capability is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and collecting VPN session data before malicious activities can cause harm. The neural network is trained in advance on historical VPN data to recognize patterns of anomalous behavior, enabling proactive security detection rather than reactive response
Solution Approach 2:
A neural network model serves as an intermediary between raw VPN session data and security decisions. This intermediary processes and analyzes complex VPN characteristics (connection attempts, data transfer patterns, session duration) to generate security risk scores, bridging the gap between simple monitoring and complex threat detection
2Productivity
If manual monitoring of VPN sessions is performed, then false positives are reduced, but response time increases
Solution Approach 1:
The system performs self-service by automatically monitoring, analyzing, and responding to VPN session anomalies without requiring continuous manual intervention. The neural network autonomously processes VPN data streams, generates risk assessments, and can automatically terminate suspicious sessions, freeing operators from manual monitoring while maintaining high detection accuracy through machine learning
3Measurement precision
If comprehensive VPN session data is collected, then detection accuracy is improved, but data processing load increases
Solution Approach 1:
The system extracts and focuses on the most relevant features from comprehensive VPN session data, such as connection attempt frequencies, data transfer volumes, and session duration metrics. By selecting only the most discriminative features for analysis, the system maintains high detection accuracy while reducing the computational burden of processing all available data
Data Source
AI summary
A virtual private network (VPN) security system obtains data regarding a VPN session including (i) for each of a plurality of first subnets, a number of allowed connection attempts by a computer system to that first subnet, (ii) for each of a plurality of second subnets, a number of blocked connection attempts by the computer system to that second subnet, (iii) for each of a plurality of first network ports, a number of allowed connection attempts by the computer system using that first network port, and (iv) for each of a plurality of second network ports, a number of blocked connection attempts by the computer system using that second network port. The security system determines, using a neural network, a metric representing an estimated likelihood that the VPN session is associated with a malicious activity, and controls the VPN session based on the metric.