| Title | Sparse Support Vector Machine for Network Behavior Anomaly Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Deng, M., Wu, X., Feng, P., Zeng, W. |
| Conference Name | 2020 IEEE 8th International Conference on Information, Communication and Networks (ICICN) |
| Date Published | aug |
| Keywords | adopted training weights, anomaly detection, composability, computer network security, Data models, feature extraction, feature selection, general machine learning approach, learning (artificial intelligence), NBAD features, network behavior anomaly detection, Optimization, pattern classification, Predictive Metrics, pubcrawl, related weights matching, Resiliency, sparse support vector machine, sparse SVM, support vector machine approach, Support vector machines, SVM, Training, Training data |
| Abstract | Network behavior anomaly detection (NBAD) require fast mechanisms for learning from the large scale data. However, the training velocity of general machine learning approach is largely limited by the adopted training weights of all features in the NBAD. In this paper, we notice, however, that the related weights matching of NBAD features is sparse, which is not necessary for holding all weights. Hence, in this paper, we consider an efficient support vector machine (SVM) approach for NBAD by imposing 1 -norm. Essentially, we propose to use sparse SVM (S-SVM), where sparsity in model, i.e. in weights is used to interfere with special feature selection and that can achieve feature selection and classification efficiently. |
| DOI | 10.1109/ICICN51133.2020.9205065 |
| Citation Key | deng_sparse_2020 |
Sparse Support Vector Machine for Network Behavior Anomaly Detection