Name
Title | ||
|---|---|---|
HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning. |
Abstract | ||
|---|---|---|
Network traffic anomaly detection is an important technique of ensuring network security. However, there are usually three problems with existing machine learning based anomaly detection algorithms. First, most of the models are built for stale data sets, making them less adaptable in real-world environments; Second, most of the anomaly detection algorithms do not have the ability to learn new models again based on changes in the attack environment; Third, from the perspective of data multi-dimensionality, a single detection algorithm has a peak value and cannot be well adapted to the needs of a complex network attack environment. Thus, we propose a new anomaly detection framework, and this framework is based on the organic integration of multiple deep learning techniques. In the first step, we used the Damped Incremental Statistics algorithm to extract features from network traffic; Second, we train Autoencoder with a small amount of label data; Third, we use Autoencoder to mark the abnormal score of network traffic; Fourth, the data with the abnormal score label is used to train the LSTM; Finally, the weighted method is used to get the final abnormal score. The experimental results show that our HELAD algorithm has better adaptability and accuracy than other state of the art algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.comnet.2019.107049 | Computer Networks |
Keywords | Field | DocType |
Traffic anomaly detection,Ensemble learning,LSTM forecast,Deep learning,Adjustable threshold | Adaptability,Data mining,Anomaly detection,Data set,Autoencoder,Computer science,Network security,Complex network,Artificial intelligence,Deep learning,Ensemble learning,Distributed computing | Journal |
Volume | ISSN | Citations |
169 | 1389-1286 | 6 |
PageRank | References | Authors |
0.43 | 36 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhong | 1 | 9 | 2.16 |
| Wenqi Chen | 2 | 9 | 1.49 |
| Zhiliang Wang | 3 | 201 | 34.74 |
| Yifan Chen | 4 | 58 | 19.82 |
| Kai Wang | 5 | 9 | 1.48 |
| YaHui Li | 6 | 14 | 6.42 |
| Xia Yin | 7 | 320 | 44.72 |
| Xingang Shi | 8 | 166 | 22.66 |
| Jiahai Yang | 9 | 200 | 53.58 |
| Keqin Li | 10 | 2778 | 242.13 |