VPN Threat Detection via Behavioral Anonymization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing VPN services make it difficult to identify and mitigate malicious network activities, as attackers can launch attacks from VPN endpoints, masking their true source IP addresses.
Innovation Solution
A network-enabled threat mitigation system that includes a security manager coupled with internet points of presence (PoPs) to detect and mitigate VPN-enabled threats by filtering and blocking attacker computers, while preserving user privacy by not requiring source IP address identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If VPN services are used to hide user identity and obfuscate source IP addresses, then user privacy and security are improved, but the ability to detect and identify malicious actors is worsened
Solution Approach 1:
The patent segments the detection process into multiple independent components: behavioral analysis module that monitors activity patterns, connection pattern analyzer that tracks communication sequences, and threat intelligence correlator that processes external data. Each component operates independently on anonymized data, allowing comprehensive threat detection without requiring source IP identification, thus resolving the contradiction between privacy protection and malicious actor identification.
Solution Approach 2:
The patent introduces an intermediary anonymization layer that processes all traffic metadata between the VPN service and detection systems. This intermediary component strips identifying information (source IPs) while preserving behavioral patterns, enabling threat detection through anonymized behavioral signatures rather than direct source identification, thereby maintaining both user privacy and detection capability.
2Loss of information
If source IP addresses are not collected to preserve user privacy, then user anonymity is improved, but the precision of threat detection and blocking is worsened
Solution Approach 1:
The patent fundamentally changes the detection parameters from identifying-based (source IP addresses) to behavior-based (connection patterns, timing sequences, data flow characteristics). The system monitors and analyzes temporal patterns, communication frequencies, and behavioral sequences as alternative parameters, achieving precise threat detection without requiring source IP information, thus resolving the contradiction between information loss and detection precision.
Solution Approach 2:
The patent substitutes the traditional mechanical identification system (relying on source IP addresses as the primary identification mechanism) with a behavioral fingerprinting system. Instead of directly identifying threats through source IPs, the system uses machine learning models to analyze and compare behavioral patterns, replacing the identification mechanism while maintaining or improving detection accuracy without compromising privacy.
3Reliability
If behavioral monitoring is implemented without source IP identification, then user privacy is preserved, but the complexity of the detection system increases
Solution Approach 1:
The patent implements universal anonymized behavioral analysis components that serve multiple detection functions simultaneously. The same anonymization module processes all traffic types, the same behavioral pattern recognition engine analyzes different threat categories, and the same threat intelligence correlator handles various data sources. This multi-functionality reduces overall system complexity despite the sophisticated detection capabilities, while maintaining privacy preservation through consistent anonymized processing.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method includes accessing a first intelligence feed including a plurality of cybersecurity incidents. A second intelligence feed is generated including a plurality of technical indicators defined on one or more virtual private network internet point of presence ("VPN internet PoP") that connects a plurality of VPN tunnels to an internet. The first and second intelligence feeds are compared, a particular incident is determined, and a time frame of the particular incident is determined. Use of a particular VPN internet PoP by a plurality of sources including a plurality of clients is monitored to determine a plurality of time-based behaviors. The plurality of time-based behaviors are compared to the particular incident and to the time frame to determine a match. A particular source is blocked at the particular VPN internet PoP based on the determination of the match.