Deep Learning Network Security Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional network security situation assessment methods face difficulties in effectively identifying various network attacks, leading to decreased network security.
Innovation Solution
A method and system utilizing deep learning-based models, specifically a convolutional autoencoder (CAE) and long-short term memory (LSTM), to detect and analyze network traffic for identifying attacks by calculating reconstruction and prediction errors, and assessing network situations based on security vulnerability analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional network security assessment methods are used, then the system is simple to operate, but the ability to identify various network attacks deteriorates
Solution Approach 1:
The patent combines multiple deep learning models (CAE for spatial features, LSTM for temporal features, and classification models) into a composite detection system. This composite architecture integrates the strengths of different model types to achieve high accuracy in identifying various network attacks while maintaining a structured approach to complexity management.
Solution Approach 2:
The detection system is segmented into distinct functional modules: traffic preprocessing, feature extraction using CAE, temporal pattern recognition using LSTM, attack detection through classification models, and reconstruction error analysis. This segmentation allows each component to specialize in specific aspects of attack detection, improving overall reliability while making the complex system more manageable.
2Measurement precision
If deep learning-based detection models are used, then the ability to identify network attacks is improved, but the computational complexity increases
Solution Approach 1:
The complex deep learning detection task is segmented into specialized sub-tasks handled by different models: CAE handles spatial feature extraction and dimensionality reduction, LSTM handles temporal sequence modeling, and separate classification models handle different attack type detection. This segmentation improves measurement precision for each sub-task while organizing computational complexity into manageable segments.
Solution Approach 2:
The patent transforms the network traffic data through dimensional transformations: CAE reduces high-dimensional traffic data to lower-dimensional latent representations, LSTM processes these representations across the temporal dimension, and classification models operate in transformed feature spaces. These dimensional changes enable precise attack detection by analyzing data from multiple dimensional perspectives.
3Measurement precision
If multiple deep learning models are combined for detection, then the accuracy of attack identification is improved, but the processing time increases
Solution Approach 1:
The detection pipeline is segmented into parallel and sequential processing stages: preprocessing and CAE feature extraction process traffic in batches, LSTM temporal analysis operates on extracted features, and classification models perform parallel attack type detection. This segmentation enables efficient resource utilization and reduces overall processing time while maintaining high detection accuracy through multiple analysis layers.
Solution Approach 2:
The system performs preliminary actions by pre-training the CAE and LSTM models on normal traffic patterns before actual attack detection. This preliminary learning phase enables the models to quickly recognize anomalies during real-time detection, reducing processing time for actual attack identification while maintaining high accuracy through pre-established knowledge of normal behavior patterns.
Data Source
AI summary
A network security situation assessment method of a network system includes: obtaining network traffic of the network system; detecting an attack on the network system from the obtained network traffic; identifying the detected attack; analyzing a possibility of an attack and an impact of an attack on the network system based on results of the detecting and identifying of the attack; and assessing a network situation of the network system based on a result of the analyzing, wherein the detecting of the attack on the network system includes detecting the attack from the network traffic using deep learning-based first model and second model.