Packet Flow Anomaly Detection Using VAE Reconstruction Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Midsize to large IT organizations face challenges in securing their networks from cyber threats, particularly network packet-based attacks, as conventional rule-based systems are not scalable for high accuracy and machine learning-based systems struggle with high classification speed and zero-day attacks.
Innovation Solution
Implementing a variational autoencoder trained to reconstruct a benign packet flow representation, using transfer entropy and Granger causality to detect anomalies in network packet flows, complementing rule-based firewall approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based systems are used for network security, then they provide clear security policies, but they are not scalable for high accuracy
Solution Approach 1:
The patent replaces rule-based mechanical security systems with a machine learning-based variational autoencoder that automatically learns packet flow patterns. This substitution enables the system to achieve high detection accuracy through statistical pattern recognition rather than manual rule configuration, resolving the contradiction between detection accuracy and system scalability.
Solution Approach 2:
The system changes from fixed rule-based parameters to dynamic learned parameters through the variational autoencoder. The model learns optimal packet flow representation parameters automatically from data, enabling scalable high-accuracy detection without manual rule tuning for each new threat type.
2Measurement precision
If machine learning-based systems are used, then detection accuracy improves, but classification speed decreases
Solution Approach 1:
The patent extracts only the essential packet flow representation features needed for anomaly detection using the variational autoencoder, rather than processing complete packet data. This extraction of critical features maintains high detection accuracy while significantly reducing processing time and improving classification speed.
Solution Approach 2:
The system applies partial action by focusing computational resources on learning and analyzing only the most discriminative packet flow characteristics through the variational autoencoder, rather than exhaustively analyzing all packet attributes. This selective approach balances detection accuracy with processing speed.
3Reliability
If conventional firewalls are used, then they provide basic protection, but they cannot detect zero-day attacks
Solution Approach 1:
The variational autoencoder performs preliminary learning of normal packet flow patterns during the training phase before actual security monitoring begins. This preliminary action enables the system to detect zero-day attacks by identifying deviations from learned baseline behavior, providing both basic protection and advanced detection capability simultaneously.
Solution Approach 2:
The patent implements a universal security system that combines the basic protection function of conventional firewalls with the advanced anomaly detection capability of the variational autoencoder. This multi-functional approach maintains reliability through rule-based filtering while adding adaptability for detecting unknown threats through statistical pattern recognition.
Data Source
AI summary
Network security and related apparatuses, methods, and security systems are disclosed. An apparatus includes a variational autoencoder trained to reconstruct a benign packet flow representation of a benign packet flow corresponding to a benign stream of packets. The processing circuitry is configured to apply a packet flow representation of a packet flow corresponding to a received stream of packets to the variational autoencoder to generate a reconstructed packet flow representation. The packet flow representation includes one or more of a determined transfer entropy corresponding to the received stream of packets, flow derived metadata, or a Granger causality of the packet flow. The processing circuitry is also configured to determine a reconstruction loss of the reconstructed packet flow representation and determine whether the received stream of packets is anomalous responsive to the determined reconstruction loss.