Name
Abstract | ||
|---|---|---|
With the advent of the era of big data, the application of Machine Learning (ML) is widely applied to the abnormal traffic detection. Detecting network anomalies plays an important role in network security. However, the large-scale traffic data detection is still a difficult problem at present. In this paper, we design a new algorithm that we called hinge classification algorithm based on mini-batch gradient descent (HCA-BAGD) to detect network anomalies. Compared with traditional traffic classification methods, such as Neural Network, Decision Tree, Logistic Regression, the algorithm can significantly boost the scale and speed of deep network training. We also solve the problem of data skew in Shuffle phase which has plagued the industry for a long time. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69811-3_42 | ADVANCES ON BROAD-BAND WIRELESS COMPUTING, COMMUNICATION AND APPLICATIONS, BWCCA-2017 |
Keywords | Field | DocType |
Detect anomalies,HCA-BAGD,Gradient descent,Hinge classification | Traffic classification,Gradient method,Decision tree,Gradient descent,Neighbourhood components analysis,Computer science,Network security,Algorithm,Backpropagation,Artificial neural network | Conference |
Volume | ISSN | Citations |
12 | 2367-4512 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodan Yan | 1 | 12 | 1.92 |
| Tianxin Zhang | 2 | 0 | 0.68 |
| Baojiang Cui | 3 | 112 | 40.18 |
| Jiangdong Deng | 4 | 9 | 2.53 |