Analysis of network traffic features for anomaly detection
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605477914 | ||
| 003 | CHVBK | ||
| 005 | 20210128100403.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-014-5473-9 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5473-9 | ||
| 245 | 0 | 0 | |a Analysis of network traffic features for anomaly detection |h [Elektronische Daten] |c [Félix Iglesias, Tanja Zseby] |
| 520 | 3 | |a Anomaly detection in communication networks provides the basis for the uncovering of novel attacks, misconfigurations and network failures. Resource constraints for data storage, transmission and processing make it beneficial to restrict input data to features that are (a) highly relevant for the detection task and (b) easily derivable from network observations without expensive operations. Removing strong correlated, redundant and irrelevant features also improves the detection quality for many algorithms that are based on learning techniques. In this paper we address the feature selection problem for network traffic based anomaly detection. We propose a multi-stage feature selection method using filters and stepwise regression wrappers. Our analysis is based on 41 widely-adopted traffic features that are presented in several commonly used traffic data sets. With our combined feature selection method we could reduce the original feature vectors from 41 to only 16 features. We tested our results with five fundamentally different classifiers, observing no significant reduction of the detection performance. In order to quantify the practical benefits of our results, we analyzed the costs for generating individual features from standard IP Flow Information Export records, available at many routers. We show that we can eliminate 13 very costly features and thus reducing the computational effort for on-line feature generation from live traffic observations at network nodes. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Feature selection |2 nationallicence | |
| 690 | 7 | |a Anomaly detection |2 nationallicence | |
| 690 | 7 | |a Network security |2 nationallicence | |
| 690 | 7 | |a Data preprocessing |2 nationallicence | |
| 690 | 7 | |a Supervised classification |2 nationallicence | |
| 700 | 1 | |a Iglesias |D Félix |u Institute of Telecommunications, Vienna University of Technology, Gusshausstrae 25 / E389, 1040, Wien, Austria |4 aut | |
| 700 | 1 | |a Zseby |D Tanja |u Institute of Telecommunications, Vienna University of Technology, Gusshausstrae 25 / E389, 1040, Wien, Austria |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 59-84 |x 0885-6125 |q 101:1-3<59 |1 2015 |2 101 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5473-9 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5473-9 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Iglesias |D Félix |u Institute of Telecommunications, Vienna University of Technology, Gusshausstrae 25 / E389, 1040, Wien, Austria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zseby |D Tanja |u Institute of Telecommunications, Vienna University of Technology, Gusshausstrae 25 / E389, 1040, Wien, Austria |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 59-84 |x 0885-6125 |q 101:1-3<59 |1 2015 |2 101 |o 10994 | ||