Gaussian Autoencoder for Network Flow Anomaly Detection
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Solution Overview
Problem
Existing computer systems face challenges in efficiently and timely detecting vulnerabilities and malicious activities within network traffic, especially with the emergence of new threats and the increasing connectivity of IoT devices, which are often unprotected by robust firewalls and antivirus software.
Innovation Solution
A Gaussian autoencoder neural network is employed to convert high-dimensional network data into a low-dimensional space, generating a Gaussian distribution, allowing for the detection of anomalies and malicious activity by evaluating data sequences relative to a trained Gaussian distribution, thereby identifying and blocking potentially malicious data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional firewall and antivirus programs are used to detect malicious activity, then known threats can be blocked, but new and emerging threats cannot be detected timely
Solution Approach 1:
The system performs preliminary training of the autoencoder model on normal network traffic patterns before deployment. This preliminary action establishes a baseline of legitimate behavior, enabling the system to detect deviations caused by new threats without requiring prior knowledge of specific attack signatures.
Solution Approach 2:
The patent transforms network traffic from high-dimensional raw data into a compressed low-dimensional representation through the autoencoder. This parameter transformation enables the detection system to focus on essential patterns while filtering out noise, improving both detection accuracy and adaptability to new threats.
2Measurement precision
If high-dimensional network data is analyzed directly, then detailed information is available, but processing efficiency and speed are reduced
Solution Approach 1:
The patent applies dimensionality reduction by transforming network traffic data from high-dimensional space into a compressed low-dimensional latent space using the autoencoder. This dimensional transformation maintains the essential characteristics needed for anomaly detection while dramatically reducing computational complexity and processing time.
Solution Approach 2:
The autoencoder extracts and retains only the most significant features from the high-dimensional network data by learning an efficient compressed representation. This extraction process removes redundant information while preserving the essential patterns needed for detecting malicious activity, thereby improving processing efficiency without sacrificing detection accuracy.
Data Source
AI summary
A method of identifying malicious activity in a computer data sequence includes providing provided the computer data sequence to a network configured to convert the computer data sequence from a high-dimensional space to a low-dimensional space, and processing the computer data sequence in the low-dimensional space to generate an approximately Gaussian distribution. The processed computer data sequence converted to the low dimensional space is evaluated relative to the approximately Gaussian distribution to determine whether the computer data sequence is likely malicious or likely benign, and an output is provided indicating whether the computer data sequence is likely malicious or likely benign.