SNMP Proxy Endpoint Data Exfiltration Detection
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Solution Overview
Problem
Current methods fail to effectively detect and prevent data exfiltration attacks, which resemble typical network traffic, making it challenging to identify and block malicious activities that can lead to substantial data loss, especially as they often exploit vulnerabilities in endpoint devices and rely on social engineering techniques.
Innovation Solution
The implementation of a method and system that uses a Simple Network Management Protocol (SNMP) proxy to detect downloads and mirror network traffic, which is then analyzed by a deep learning model to identify malware, and sends alerts to prevent data exfiltration attacks, while also tracking suspicious uploads and preventing access to blacklisted websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security methods are used to detect data exfiltration, then they can monitor network traffic, but they fail to effectively distinguish malicious activities from typical network traffic
Solution Approach 1:
The patent introduces SNMP agents as intermediary components installed on endpoint devices that collect local system data (downloaded files, uploaded files, web browsing history) and transmit it to a centralized manager. This intermediary layer enables precise detection of data exfiltration by providing structured, device-specific information that traditional network security methods cannot obtain, thereby improving detection accuracy without requiring complex analysis of all network traffic.
2Reliability
If comprehensive network monitoring is implemented to detect all malicious activities, then detection coverage improves, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential and relevant data elements needed for data exfiltration detection from endpoint devices, such as downloaded files, uploaded files, and web browsing history. By selectively extracting only this critical information rather than monitoring all network traffic, the system achieves comprehensive detection coverage for data exfiltration threats while minimizing network bandwidth consumption.
3Speed
If real-time data monitoring is performed to prevent data exfiltration, then response time improves, but CPU resource consumption increases
Solution Approach 1:
The patent implements preliminary action by having SNMP agents on endpoint devices continuously collect and buffer data elements (downloaded files, uploaded files, web browsing history) in advance. This pre-collection approach allows the centralized manager to perform rapid real-time analysis when needed, achieving fast response times for data exfiltration prevention without requiring continuous high CPU resource consumption across the entire network.
Data Source
AI summary
A method and system for detecting and preventing data exfiltration attacks is disclosed. The system includes a manager device and an endpoint device, and an attack prevention device. The method includes detecting, at plurality of endpoint devices, downloads made at endpoint devices using Simple Network Management Protocol (SNMP) proxy. Next, the method includes mirroring network traffic associated with the downloads. The method further includes providing the mirrored network traffic as an input to a deep learning model, wherein the deep learning model detects presence of malware in the downloads. Finally, the method involves sending, by the manager device, an alert to one or more devices to prevent, recover, or mitigate data exfiltration attacks.