In this work, we propose an SDN-based WLAN monitoring and management framework called RFlow+ to address WiFi service dissatisfaction caused by the limited view (lack of scalability) of network traffic monitoring and absence of intelligent and timely network treatments. Existing solutions (e.g., OpenFlow and sFlow) have limited view, no generic flow description, and poor trade-off between measurement accuracy and network overhead depending on the selection of the sampling rate. To resolve these issues, we devise a two-level counting mechanism, namely a distributed local counter (on-site and real-time) and central collector (a summation of local counters). With this, we proposed a highly scalable monitoring and management framework to handle immediate actions based on short-term (e.g., 50 ms) monitoring and eventual actions based on long-term (e.g., 1 month) monitoring. The former uses the local view of each access point (AP), and the latter uses the global view of the collector. Experimental results verify that RFlow+ can achieve high accuracy (less than 5% standard error for short-term and less than 1% for long-term) and fast detection of flows of interest (within 23 ms) with manageable network overhead. We prove the practicality of RFlow+ by showing the effectiveness of a MAC flooding attacker quarantine in a real-world testbed.
|Title of host publication||INFOCOM 2017 - IEEE Conference on Computer Communications|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2 Oct 2017|
|Event||2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States|
Duration: 1 May 2017 → 4 May 2017
|Name||Proceedings - IEEE INFOCOM|
|Conference||2017 IEEE Conference on Computer Communications, INFOCOM 2017|
|Period||1/05/17 → 4/05/17|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was supported by Global Research Lab. (GRL) Program of the National Research Foundation (NRF) funded by Ministry of Science, ICT (Information and Communication Technologies) and Future Planning(NRF-2016K1A1A2912757). This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education under Grant NRF-2016R1C1B2011415. DaeHun Nyang is the corresponding author.
© 2017 IEEE.