A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., & Srivastava, J. In Proceedings of the 2003 SIAM International Conference on Data Mining, of Proceedings, pages 25–36. Society for Industrial and Applied Mathematics, May, 2003. ![A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of network connections [9] as well as on real network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA'98 data and real network data.
@incollection{lazarevic_comparative_2003,
series = {Proceedings},
title = {A {Comparative} {Study} of {Anomaly} {Detection} {Schemes} in {Network} {Intrusion} {Detection}},
isbn = {978-0-89871-545-3},
url = {https://epubs.siam.org/doi/abs/10.1137/1.9781611972733.3},
abstract = {Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of network connections [9] as well as on real network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA'98 data and real network data.},
urldate = {2019-04-30TZ},
booktitle = {Proceedings of the 2003 {SIAM} {International} {Conference} on {Data} {Mining}},
publisher = {Society for Industrial and Applied Mathematics},
author = {Lazarevic, A. and Ertoz, L. and Kumar, V. and Ozgur, A. and Srivastava, J.},
month = may,
year = {2003},
doi = {10.1137/1.9781611972733.3},
pages = {25--36}
}
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