| Title | A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Liu, Shuyong, Jiang, Hongrui, Li, Sizhao, Yang, Yang, Shen, Linshan |
| Conference Name | 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID) |
| Keywords | anomaly detection, Artificial neural networks, component, convolutional neural networks, cyber physical systems, Cyber-physical systems, Data models, Deep Learning, Mathematical model, Metrics, Network security, Neural Network Security, policy-based governance, pubcrawl, Resiliency, security, Training |
| Abstract | Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%. |
| DOI | 10.1109/ASID50160.2020.9271685 |
| Citation Key | liu_feature_2020 |
A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks