Unsupervised Spoofing Detection in Mobile Networks
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Solution Overview
Problem
Traditional signature-based intrusion detection methods in mobile networks are ineffective in detecting spoofing attacks, as they can only recognize previously known threats and are vulnerable to new attacks until their signature databases are updated, and they struggle with high-dimensional and high-variance network traffic data.
Innovation Solution
The implementation of a deep auto-encoding Gaussian mixture model (DAGMM) for unsupervised anomaly detection, which extracts relevant features from network traffic data using domain knowledge, reduces dimensionality and variance, and employs a state-of-the-art deep learning technique to identify anomalous traffic patterns indicative of spoofing attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection methods are used, then previously known intrusions can be detected, but new spoofing attacks cannot be detected until the signature database is updated
Solution Approach 1:
The system performs preliminary action by training the DAGMM anomaly detection model in advance with normal traffic data, establishing a baseline of legitimate network behavior before actual attacks occur. This preliminary training enables the system to detect new spoofing attacks without requiring pre-configured signatures, resolving the contradiction between reliable known-attack detection and adaptability to new threats
Solution Approach 2:
The system implements self-service through unsupervised anomaly detection that automatically identifies new attack patterns without human intervention or manual signature updates. The DAGMM model continuously learns from traffic data and autonomously detects deviations from normal behavior, enabling the system to adapt to new attacks while maintaining reliable detection of known threats
2Ease of manufacture
If traditional intrusion detection systems are deployed, then detection capabilities are limited to signature-based methods, but they struggle with high-dimensional and high-variance network traffic data
Solution Approach 1:
The system replaces the mechanical signature-matching approach with a deep learning-based DAGMM model that automatically processes high-dimensional network traffic data. This substitution transforms the detection mechanism from manual signature comparison to automated anomaly detection, enabling the system to handle complex high-dimensional data while maintaining ease of deployment through a unified detection framework
Data Source
AI summary
A method for detecting spoofing attacks from network traffic log data is presented. The method includes training a spoofing attack detector with the network traffic log data received from one or more mobile networks by extracting features that are relevant to spoofing attacks for training data, building a first set of vector representations for the network traffic log data, training an anomaly detection model by employing DAGMM, and obtaining learned parameters of DAGMM. The method includes testing the spoofing attack detector with the network traffic log data received from the one or more mobile networks by extracting features that are relevant to spoofing attacks for testing data, building a second set of vector representations for the network traffic log data, obtaining latent representations of the testing data, computing a z-score of the testing data, and creating a spoofing attack alert report listing traffic logs generating z-scores exceeding a predetermined threshold.