A Novel Learning Algorithm on Probability Measure for Intrusion Detection
Xiang Fang, Ying Jia
Available Online August 2016.
- https://doi.org/10.2991/cset-16.2016.31How to use a DOI?
- Intrusion detection, support vector machine, probability measures, kernal fuction
- Attacks cyber-based have already seriously threaten the security of network environment and network application with the rapid development and wide application of network services. Intrusion detection plays a vital role in the network security. The machine learning methods have been utilized in Intrusion Detection. Because the network intrusion system has to deal with a huge amount of data, its consumption is too large in the space and time. We present an algorithm that learns from probability measures instead of the specific samples in the traditional support vector machine (SVM).The novel algorithm can increase efficiency by the scale down dataset. The simulation test results on the KDD cup99 dataset show that our method is faster than traditional SVM algorithm at the premise of recognition accuracy.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xiang Fang AU - Ying Jia PY - 2016/08 DA - 2016/08 TI - A Novel Learning Algorithm on Probability Measure for Intrusion Detection BT - 2016 International Conference on Computer Science and Electronic Technology PB - Atlantis Press SP - 129 EP - 132 SN - 2352-538X UR - https://doi.org/10.2991/cset-16.2016.31 DO - https://doi.org/10.2991/cset-16.2016.31 ID - Fang2016/08 ER -