A Framework For Network Intrusion Detection Based on Unsupervised Learning

Anomaly detection is the primary method of detecting intrusion. Unsupervised models, such as auto-encoders network, auto-encoder, and GMM, are currently the most widely used anomaly detection techniques. In reality, the samples used to train the unsupervised model may not be pure enough and may incl...

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Bibliographic Details
Published in:2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) pp. 188 - 193
Main Authors: Hui, Wang, Dongming, Wang, Dejian, Li, Lin, Zeng, Zhe, Wang
Format: Conference Proceeding
Language:English
Published: IEEE 28.05.2021
Subjects:
Conferences
Data collection
Deep AutoEncoders Network
Feature extraction
Gaussian Mixture Model(GMM
Intrusion Detection System
Measurement
Network intrusion detection
Reconstruction Errors
Systems architecture
Training
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Summary:Anomaly detection is the primary method of detecting intrusion. Unsupervised models, such as auto-encoders network, auto-encoder, and GMM, are currently the most widely used anomaly detection techniques. In reality, the samples used to train the unsupervised model may not be pure enough and may include some abnormal samples. However, the classification effect is poor since these approaches do not completely understand the association between reconstruction errors, reconstruction characteristics, and irregular sample density distribution. This paper proposes a novel intrusion detection system architecture that includes data collection, processing, and feature extraction by integrating data reconstruction features, reconstruction errors, auto-encoder parameters, and GMM. Our system outperforms other unsupervised learning-based detection approaches in terms of accuracy, recall, F1-score, and other assessment metrics after training and testing on multiple intrusion detection data sets.
DOI:10.1109/AIID51893.2021.9456542