Concept Drift Based on Subspace Learning for Intrusion Detection
Bin Wu, Hai-Zhuo Lin, Lin Feng
Available Online November 2016.
- https://doi.org/10.2991/ceis-16.2016.85How to use a DOI?
- intrusion detection; concept drift; subspace learning
- In recent years, Intrusion Detection System(IDS) thrives and becomes the main approach for detecting and defending internet attack. And network streams are the best data sources for studying network attack. In order to detect intrusions, concept drifting method is applied. What is more, the subspace learning based concept drifting method is fit for dealing with high dimensional data streams. It can not only detect the concept drift, but also reduce the dimensionality at the same time, which makes the detection more efficient. We also design model for judging concept drift, which checks the deviation of the error term of projection variance and the deviation of the error term of projection cosine. The experiment of KDD data set validates that our method is more efficient and accurate.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Bin Wu AU - Hai-Zhuo Lin AU - Lin Feng PY - 2016/11 DA - 2016/11 TI - Concept Drift Based on Subspace Learning for Intrusion Detection BT - Proceedings of the 2016 International Conference on Computer Engineering and Information Systems PB - Atlantis Press SP - 421 EP - 425 SN - 2352-538X UR - https://doi.org/10.2991/ceis-16.2016.85 DO - https://doi.org/10.2991/ceis-16.2016.85 ID - Wu2016/11 ER -