Autoencoder Clustering for Real-Time Network Intrusion Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current intrusion detection systems face challenges in handling large volumes of unknown network data, leading to low detection rates due to model performance degradation, clustering complexity, and resource constraints, particularly in real-time scenarios.
Innovation Solution
A method using an autoencoder-based machine learning system that minimizes reconstruction loss while encouraging homogeneous and separate clusters, employing a stopping condition to tune internal weights and iteratively refine clustering, allowing efficient processing of large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If clustering methods are applied to analyze unknown network data, then insight into data structure is improved, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent segments the clustering process into multiple stages: initial coarse clustering with simplified parameters, followed by refined clustering with optimized parameters. This multi-stage segmentation reduces memory requirements by processing data in manageable batches rather than loading entire datasets into memory simultaneously.
Solution Approach 2:
The patent performs preliminary dimensionality reduction and feature selection before applying clustering algorithms. By pre-processing the data to extract only the most relevant features and reducing dimensionality, the computational complexity of subsequent clustering operations is significantly reduced while preserving essential data structure information.
2Loss of information
If traditional clustering algorithms are used on large datasets, then cluster analysis is improved, but processing time increases making real-time analysis difficult
Solution Approach 1:
The patent dynamically adjusts clustering parameters such as cluster count, density thresholds, and similarity metrics based on the characteristics of the input data. By adapting parameters to match data distribution and size, the algorithm achieves acceptable clustering quality faster than fixed-parameter approaches that require extensive tuning and multiple iterations.
Solution Approach 2:
The patent implements a stopping criterion that allows the clustering process to terminate when a satisfactory level of cluster separation is achieved, rather than continuing until complete convergence. This partial action approach produces sufficiently accurate results in real-time while avoiding the excessive processing time required for full convergence of traditional algorithms.
3Reliability
If more data packets are analyzed in real-time, then detection rate is improved, but resource constraints are exceeded
Solution Approach 1:
The patent extracts and analyzes only the most discriminative features from network data packets using feature selection techniques. By taking out only the essential features that contribute to detection accuracy rather than processing all packet attributes, the system maintains high detection rates while reducing computational resource consumption to feasible levels.
Solution Approach 2:
The patent segments the network traffic analysis into flow-level aggregation and packet-level inspection only when necessary. By segmenting the analysis scope based on detected anomalies and traffic patterns, the system processes larger volumes of data efficiently by performing lightweight analysis on most packets and detailed analysis only on suspicious segments.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method comprising: - obtaining a dataset of input samples, one of said input samples comprising a number of input data features, - applying said input samples to a machine learning system comprising a first machine learning model, or encoder, configured to output encoded samples, one of said output encoded samples comprising fewer encoded features than the number of input data features, - applying said output encoded samples to a second machine learning model, or decoder, of said machine learning system, configured to produce reconstructed input samples from said encoded samples, - determining a reconstruction loss based on a difference between the input samples and the reconstructed samples, -clustering said encoded samples into a plurality of clusters, - determining a clustering error, said clustering error being defined as taking on a lower value the more homogeneous and separated the clusters are, - obtaining a total loss based on the reconstruction loss and clustering error, and -while a stopping condition comprising the total loss being less than a best total loss, is not reached, tuning internal weights of said encoder and said decoder based on said total loss, and reiterating the previous steps.