@inproceedings{59fd3a7905784635a83d446ad25613d9,
title = "A unified approach to network anomaly detection",
abstract = "This paper presents a unified approach for the detection of network anomalies. Current state of the art methods are often able to detect one class of anomalies at the cost of others. Our approach is based on using a Linear Dynamical System (LDS) to model network traffic. An LDS is equivalent to Hidden Markov Model (HMM) for continuous-valued data and can be computed using incremental methods to manage high-throughput (volume) and velocity that characterizes Big Data. Detailed experiments on synthetic and real network traces shows a significant improvement in detection capability over competing approaches. In the process we also address the issue of robustness of network anomaly detection systems in a principled fashion.",
author = "Tahereh Babaie and Sanjay Chawla and Sebastien Ardon and Yue Yu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",
year = "2014",
doi = "10.1109/BigData.2014.7004288",
language = "English",
series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "650--655",
editor = "Wo Chang and Jun Huan and Nick Cercone and Saumyadipta Pyne and Vasant Honavar and Jimmy Lin and Hu, {Xiaohua Tony} and Charu Aggarwal and Bamshad Mobasher and Jian Pei and Raghunath Nambiar",
booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
address = "United States",
}