| Title | A Framework For Network Intrusion Detection Based on Unsupervised Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Hui, Wang, Dongming, Wang, Dejian, Li, Lin, Zeng, Zhe, Wang |
| Conference Name | 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) |
| Date Published | May 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-1537-8 |
| Keywords | composability, Conferences, Data collection, Deep AutoEncoders Network, feature extraction, Gaussian Mixture Model(GMM, Measurement, Metrics, network intrusion detection, network on chip security, pubcrawl, resilience, Resiliency, Scalability, Systems architecture, Training |
| Abstract | 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. |
| URL | https://ieeexplore.ieee.org/document/9456542 |
| Citation Key | hui_framework_2021 |
A Framework For Network Intrusion Detection Based on Unsupervised Learning