Stream4Flow: Real-time IP Flow Host Monitoring using Apache Spark
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/document/8406132 |
| Doi | http://dx.doi.org/10.1109/NOMS.2018.8406132 |
| Keywords | host monitoring; situation awareness; real-time; Stream4Flow |
| Attached files | |
| Description | In this paper, we present Stream4Flow, a framework for cyber situational awareness based on Apache Spark Streaming. We demonstrate utilization of Stream4Flow for real-time IP flow host monitoring in a large campus network. Contemporary IP flow analysis systems are not designed for the continuous host monitoring. Gaining the detailed overview of each host is not straightforward with these systems due to connection-based paradigm and performance challenges. We show that distributed stream processing is a natural solution for detailed IP flow host monitoring. Moreover, we describe a real-time host monitoring workflow in data streams in detail and present advantages of flow-based host monitoring in Apache Spark including real-time host profiling, dynamic level of detail and granularity. |
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