TY - GEN
T1 - CLEF
T2 - 17th International Conference on Cryptology and Network Security, CANS 2018
AU - Wu, Hao
AU - Hsiao, Hsu Chun
AU - Asoni, Daniele E.
AU - Scherrer, Simon
AU - Perrig, Adrian
AU - Hu, Yih Chun
N1 - Funding Information:
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013), ERC grant agreement 617605, the Ministry of Science and Technology of Taiwan under grant number MOST 107-2636-E-002-005, and the US National Science Foundation under grant numbers CNS-1717313 and CNS-0953600. We also gratefully acknowledge support from ETH Zurich and from the Zurich Information Security and Privacy Center (ZISC).
Funding Information:
We thank Pratyaksh Sharma and Prateesh Goyal for early work on this project as part of their summer internship at ETH in Summer 2015. We also thank the anonymous reviewers, whose feedback helped to improve the paper. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013), ERC grant agreement 617605, the Ministry of Science and Technology of Taiwan under grant number MOST 107-2636-E-002-005, and the US National Science Foundation under grant numbers CNS-1717313 and CNS-0953600. We also gratefully acknowledge support from ETH Zurich and from the Zurich Information Security and Privacy Center (ZISC).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - The detection of network flows that send excessive amounts of traffic is of increasing importance to enforce QoS and to counter DDoS attacks. Large-flow detection has been previously explored, but the proposed approaches can be used on high-capacity core routers only at the cost of significantly reduced accuracy, due to their otherwise too high memory and processing overhead. We propose CLEF, a new large-flow detection scheme with low memory requirements, which maintains high accuracy under the strict conditions of high-capacity core routers. We compare our scheme with previous proposals through extensive theoretical analysis, and with an evaluation based on worst-case-scenario attack traffic. We show that CLEF outperforms previously proposed systems in settings with limited memory.
AB - The detection of network flows that send excessive amounts of traffic is of increasing importance to enforce QoS and to counter DDoS attacks. Large-flow detection has been previously explored, but the proposed approaches can be used on high-capacity core routers only at the cost of significantly reduced accuracy, due to their otherwise too high memory and processing overhead. We propose CLEF, a new large-flow detection scheme with low memory requirements, which maintains high accuracy under the strict conditions of high-capacity core routers. We compare our scheme with previous proposals through extensive theoretical analysis, and with an evaluation based on worst-case-scenario attack traffic. We show that CLEF outperforms previously proposed systems in settings with limited memory.
KW - Damage metric
KW - Large-flow detection
KW - Memory and computation efficiency
UR - http://www.scopus.com/inward/record.url?scp=85057346582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057346582&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00434-7_5
DO - 10.1007/978-3-030-00434-7_5
M3 - Conference contribution
AN - SCOPUS:85057346582
SN - 9783030004330
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
EP - 108
BT - Cryptology and Network Security - 17th International Conference, CANS 2018, Proceedings
A2 - Papadimitratos, Panos
A2 - Camenisch, Jan
PB - Springer
Y2 - 30 September 2018 through 3 October 2018
ER -