Edge Packet Regeneration for Low-Latency Data Exfiltration Control
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Solution Overview
Problem
Existing network security systems are susceptible to data exfiltration due to human error and AI inaccuracies, leading to inefficient network operations with high latency and inability to prevent unauthorized data transfer.
Innovation Solution
A system that regenerates network packets with a known-safe payload at an edge node, ensuring that only authorized data is transmitted outside the network, using a multi-step process to identify and recreate packets without inspecting each packet for malicious content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification algorithms are run on each packet to detect malicious content, then data exfiltration detection accuracy is improved, but network latency increases and throughput decreases
Solution Approach 1:
The patent extracts only the payload portion of network packets for inspection while allowing packet headers and other non-payload data to pass through unchanged. This selective extraction approach enables focused security analysis on the critical payload section without processing the entire packet, thereby reducing the computational overhead and latency associated with full packet inspection while maintaining detection accuracy for data exfiltration attempts.
2Extent of automation
If AI models are used to classify packets for security detection, then automated security monitoring is improved, but false negatives occur and data exfiltration may be missed
Solution Approach 1:
The patent implements feedback mechanisms where detected data exfiltration patterns and false negative cases are fed back into the AI classification models for continuous training and refinement. This feedback loop enables the system to learn from its mistakes, improve its classification accuracy over time, and reduce false negatives while maintaining automated security monitoring. The system adapts to new attack patterns and refines its detection capabilities based on real-world performance data.
3Object-affected harmful factors
If malware is blocked at the network perimeter, then initial malware entry is prevented, but data exfiltration by already-infected systems cannot be stopped
Solution Approach 1:
The patent applies preliminary action by implementing security measures at multiple stages: first at the network perimeter to block malware entry, and then continuously monitoring outbound traffic from internal systems before data exfiltration can occur. The system proactively inspects packets leaving the network, identifying and blocking data exfiltration attempts from already-infected systems before sensitive data can be transmitted to external malicious actors. This multi-stage preliminary action approach addresses both initial malware entry and subsequent data exfiltration.
Data Source
AI summary
The present invention is for systems and methods for securing network traffic. An example method may comprise receiving, from an edge node for a network, a packet (original packet) from a first computing device addressed to a second computing device. The first computing device may be located inside the network. The second computing device may be located outside the network. Key information may be extracted from the original packet. The extracted key information may comprise an address associated with the first computing device, an address associated with the second computing device, etc. A new packet may be created with the extracted key information. The original packet may be discarded. Known safe information may be entered in a payload of the new packet. The new packet may be caused to be routed outside of the network, to the second computing device, as instructed by the original packet.