Volumetric hierarchical heavy hitters

Ran Ben Basat; Gil Einziger; Roy Friedman; Marcelo Caggiani Luizelli; Erez Waisbard

doi:https://doi.org/10.1109/MASCOTS.2018.00045

Volumetric hierarchical heavy hitters

Ran Ben Basat, Gil Einziger, Roy Friedman, Marcelo Caggiani Luizelli, Erez Waisbard

Computer Science

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hierarchical heavy hitters (HHH) identification is useful for various network utilities such as anomaly detection, DDoS mitigation, and traffic analysis. However, the increasing support for jumbo frames enables attackers to overload the system with fewer packets, avoiding detection by packet counting techniques. This paper suggests an efficient algorithm for detecting HHH based on their traffic volume that asymptotically improves the runtime of previous works. We implement our algorithm in Open vSwitch (OVS) and incur a 4−6% overhead compared to a 42% throughput reduction experienced by the state-of-the-art.

Original language	American English
Title of host publication	Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
Pages	381-392
Number of pages	12
ISBN (Electronic)	9781538668863
DOIs	https://doi.org/10.1109/MASCOTS.2018.00045
State	Published - 7 Nov 2018
Event	26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018 - Milwaukee, United States Duration: 25 Sep 2018 → 28 Sep 2018

Publication series

Name	Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
Volume	2018-January

Conference

Conference	26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018
Country/Territory	United States
City	Milwaukee
Period	25/09/18 → 28/09/18

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Modelling and Simulation

Access to Document

https://doi.org/10.1109/MASCOTS.2018.00045

Cite this

Basat, R. B., Einziger, G., Friedman, R., Luizelli, M. C., & Waisbard, E. (2018). Volumetric hierarchical heavy hitters. In Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018 (pp. 381-392). (Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018; Vol. 2018-January). https://doi.org/10.1109/MASCOTS.2018.00045

Basat, Ran Ben ; Einziger, Gil ; Friedman, Roy et al. / Volumetric hierarchical heavy hitters. Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018. 2018. pp. 381-392 (Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018).

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title = "Volumetric hierarchical heavy hitters",

abstract = "Hierarchical heavy hitters (HHH) identification is useful for various network utilities such as anomaly detection, DDoS mitigation, and traffic analysis. However, the increasing support for jumbo frames enables attackers to overload the system with fewer packets, avoiding detection by packet counting techniques. This paper suggests an efficient algorithm for detecting HHH based on their traffic volume that asymptotically improves the runtime of previous works. We implement our algorithm in Open vSwitch (OVS) and incur a 4−6% overhead compared to a 42% throughput reduction experienced by the state-of-the-art.",

author = "Basat, {Ran Ben} and Gil Einziger and Roy Friedman and Luizelli, {Marcelo Caggiani} and Erez Waisbard",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018 ; Conference date: 25-09-2018 Through 28-09-2018",

year = "2018",

month = nov,

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doi = "https://doi.org/10.1109/MASCOTS.2018.00045",

language = "American English",

series = "Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018",

pages = "381--392",

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Basat, RB, Einziger, G , Friedman, R, Luizelli, MC & Waisbard, E 2018, Volumetric hierarchical heavy hitters. in Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018. Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018, vol. 2018-January, pp. 381-392, 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018, Milwaukee, United States, 25/09/18. https://doi.org/10.1109/MASCOTS.2018.00045

Volumetric hierarchical heavy hitters. / Basat, Ran Ben; Einziger, Gil ; Friedman, Roy et al.
Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018. 2018. p. 381-392 (Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Luizelli, Marcelo Caggiani

AU - Waisbard, Erez

PY - 2018/11/7

Y1 - 2018/11/7

N2 - Hierarchical heavy hitters (HHH) identification is useful for various network utilities such as anomaly detection, DDoS mitigation, and traffic analysis. However, the increasing support for jumbo frames enables attackers to overload the system with fewer packets, avoiding detection by packet counting techniques. This paper suggests an efficient algorithm for detecting HHH based on their traffic volume that asymptotically improves the runtime of previous works. We implement our algorithm in Open vSwitch (OVS) and incur a 4−6% overhead compared to a 42% throughput reduction experienced by the state-of-the-art.

AB - Hierarchical heavy hitters (HHH) identification is useful for various network utilities such as anomaly detection, DDoS mitigation, and traffic analysis. However, the increasing support for jumbo frames enables attackers to overload the system with fewer packets, avoiding detection by packet counting techniques. This paper suggests an efficient algorithm for detecting HHH based on their traffic volume that asymptotically improves the runtime of previous works. We implement our algorithm in Open vSwitch (OVS) and incur a 4−6% overhead compared to a 42% throughput reduction experienced by the state-of-the-art.

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M3 - Conference contribution

T3 - Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018

SP - 381

EP - 392

BT - Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018

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Basat RB, Einziger G , Friedman R, Luizelli MC, Waisbard E. Volumetric hierarchical heavy hitters. In Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018. 2018. p. 381-392. (Proceedings - 26th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2018). doi: https://doi.org/10.1109/MASCOTS.2018.00045

Volumetric hierarchical heavy hitters

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