@inproceedings{4f280ccbcd4a4cd289878a84a7f5cb39,
title = "A categorically reweighted feature extraction method for anomaly detection",
abstract = "Anomaly detection is an important research problem in diverse computer security areas. Reliability of normal and abnormal samples is generally different in practice. We propose a scheme with a categorical re-weighting parameter to utilize this categorical reliability difference for feature extraction in anomaly detection. It is shown that this re-weighting parameter provides a method to tune the decision hyperplane between normal and abnormal classes. Based on two existing feature extraction algorithms, two new feature extraction algorithms using the proposed scheme are designed, which generalize the existing methods. Experiments show that the proposed methods outperform the previous state-of-arts in terms of both classification accuracy and robustness on synthetic and real-world data sets.",
keywords = "Anomaly detection, Data mining, Feature extraction, Network security",
author = "Ruyao Cui and Lei Sun and Jie Yang and Bo Liu and Yuan Fan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018 ; Conference date: 18-06-2018 Through 21-06-2018",
year = "2018",
month = jul,
day = "16",
doi = "10.1109/DSC.2018.00111",
language = "English",
series = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "697--704",
booktitle = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
address = "United States",
}