Detailed Information on Publication Record

@inproceedings{36167,
   author = {Smeriga, Juraj and Jirsík, Tomáš},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 14th International Conference on Availability, Reliability and Security},
   edition = {ACM},
   keywords = {IP flows; machine learning; network segmentation; clustering; classification; behavior analysis},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-7164-3},
   pages = {1-9},
   publisher = {ACM},
   title = {Behavior-Aware Network Segmentation using IP Flows},
   url = {http://doi.acm.org/10.1145/3339252.3339265},
   year = {2019}
}

TY  - JOUR
ID  - 36167
AU  - Smeriga, Juraj - Jirsík, Tomáš
PY  - 2019
TI  - Behavior-Aware Network Segmentation using IP Flows
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450371643
KW  - IP flows
KW  - machine learning
KW  - network segmentation
KW  - clustering
KW  - classification
KW  - behavior analysis
UR  - http://doi.acm.org/10.1145/3339252.3339265
N2  - Network segmentation is a powerful tool for network defense. In contemporary complex, dynamic, and multilayer networks, network segmentation suffers from lack of visibility into processes in the network, which results in less strict segment definition and loosen network security. Moreover, the dynamics of the networks makes the manual identification of network segments nearly impossible. In this paper, we inspect the possibilities of the behavior-aware network segmentation using IP flows and machine learning approaches that would enable to identify segments automatically, even in a complex network. We evaluate the suitability of clustering algorithms for identification of behavior-consistent segments in a network. We show that the clustering algorithms can identify relevant behavior-consistent clusters that overlap with those identified manually by experts. Apart from the segment identification, we investigate the other essential task of network segmentation process: assignment of an unknown host to an existing segment. We evaluate the performance of four different classification mechanisms on a real-world dataset. We show that it is possible to assign an unknown host to an appropriate network segment with up to 92% precision. Moreover, we release the whole dataset and experiment steps available for public use.
ER  - 

SMERIGA, Juraj and Tomáš JIRSÍK. Behavior-Aware Network Segmentation using IP Flows. Online. In \textit{Proceedings of the 14th International Conference on Availability, Reliability and Security}. ACM. New York, NY, USA: ACM, 2019, p.~1-9. ISBN~978-1-4503-7164-3.