@inproceedings{758f0baeca4e4559bf55b8e11abe54ca,
title = "Multivariate metrics of normal and anomalous network behaviors",
abstract = "Detecting network anomalies is a fundamental part of day to day operations for Internet Service Providers and enterprises to maintain the efficiency and reliability of computer networks. Network anomaly detection is based on data characteristics of normal and anomalous network behaviors. Although many existing studies report univariate data characteristics of normal and anomalous network behaviors, there are few studies on multivariate data characteristics of normal and anomalous network behaviors. The goal of this study is to investigate multivariate data characteristics of normal and anomalous network behaviors using the Partial-Value Association Discovery (PVAD) algorithm. This paper illustrates the use of the PVAD algorithm to analyze network flow data of a medium size enterprise under the normal condition and an anomalous condition and reveal multivariate data characteristics of the normal and anomalous network flows in the form of multivariate data associations.",
keywords = "Data mining, Multivariate data characteristics of network flows, Network anomaly detection",
author = "Nong Ye and Douglas Montgomery and Kevin Mills and Mark Carson",
year = "2019",
month = may,
day = "16",
language = "English (US)",
series = "2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "55--58",
booktitle = "2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019",
note = "2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019 ; Conference date: 08-04-2019 Through 12-04-2019",
}