Heterogeneous Autoencoder Network Intrusion Detection
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Solution Overview
Problem
In the Internet of Blended Environment (IoBE), where various IoT devices are complexly interconnected, existing methods fail to effectively detect and differentiate between normal and abnormal network traffic, leading to increased security threats and cyber attacks due to diversified attack surfaces.
Innovation Solution
A network intrusion detection system utilizing a combination of convolutional variational autoencoders (VAE) and long short-term memory (LSTM)-VAE models, with a clustering module to classify data points and set anomaly thresholds based on reconstruction losses, to accurately identify abnormal data points and detect network intrusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing network intrusion detection methods are used in IoBE environments, then the system structure is simple, but the detection accuracy is insufficient due to inability to effectively differentiate between normal and abnormal network traffic
Solution Approach 1:
The patent segments the network traffic detection task by dividing data into multiple groups based on spatial and temporal characteristics. Different heterogeneous models (CNN-VAE for spatial patterns, LSTM-VAE for temporal patterns) are assigned to different data groups, allowing each model to specialize in specific traffic patterns and improve overall detection accuracy without requiring a single overly complex model
Solution Approach 2:
The patent employs a composite approach by integrating multiple heterogeneous autoencoder models with different architectural characteristics. The system combines convolutional variants for spatial feature extraction with LSTM variants for temporal sequence modeling, creating a composite detection system that leverages the strengths of each model type to achieve superior detection accuracy
2Measurement precision
If multiple heterogeneous models are used to improve detection accuracy, then the detection precision increases, but the processing load and computational complexity increase
Solution Approach 1:
The patent applies local quality by routing different types of network traffic data to different models based on their characteristics. The clustering module analyzes spatial and temporal features and directs appropriate data groups to specialized models, ensuring that computational resources are used efficiently by matching data characteristics with model strengths rather than processing all data through every model
Solution Approach 2:
The system implements partial action by selectively applying different models to different data groups rather than applying all models to all data. The routing module determines which models should process which data points based on clustering results, reducing redundant computations while maintaining detection accuracy for diverse traffic patterns
3Adaptability or versatility
If heterogeneous models with different characteristics are deployed, then the system can handle diverse attack surfaces, but the device complexity and model management difficulty increase
Solution Approach 1:
The patent introduces a clustering module and routing module as intermediaries between the data input and the heterogeneous models. These intermediary components analyze data characteristics, perform clustering based on spatial and temporal features, and route appropriate data groups to the most suitable models, simplifying the management of heterogeneous models by providing a systematic interface for data-model matching
Solution Approach 2:
The system performs preliminary action by clustering and categorizing network traffic data before routing it to appropriate models. The clustering module pre-processes data by identifying spatial and temporal patterns and grouping similar traffic characteristics together, so that when data reaches the heterogeneous models, it is already organized in an optimal format for each model type, reducing management complexity
4Measurement precision
If anomaly thresholds are set based on loss distribution for each model, then the detection precision improves, but the time required for threshold configuration and model training increases
Solution Approach 1:
The patent dynamically adjusts anomaly thresholds based on the loss distribution characteristics of each heterogeneous model. Rather than using fixed thresholds, the system adapts threshold values according to the specific statistical properties and performance characteristics of each model, improving detection precision by optimizing thresholds to match each model's behavior patterns and the characteristics of the data it processes
Data Source
AI summary
A network intrusion detection system includes: a data collection unit configured to obtain a dataset for training a plurality of machine learning-based heterogeneous models included in the network intrusion detection system; a clustering module configured to cluster data points included in the obtained dataset; a routing module configured to selectively input the data points into at least one of the plurality of models based on a result of the clustering; and a model training unit configured to define a loss function based on a reconstruction loss of each of the at least one model for an input data point, and perform an update of each of the at least one model so that the defined loss function is minimized, wherein the model training unit sets an anomaly threshold for each of the at least one model based on loss distribution of reconstruction losses for the data points.