Article

Impact of packet sampling on anomaly detection metrics

Authors:

Daniela Brauckhoff,

Bernhard Tellenbach,

Anukool LakhinaAuthors Info & Claims

IMC '06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement

Pages 159 - 164

https://doi.org/10.1145/1177080.1177101

Published: 25 October 2006 Publication History

Abstract

Packet sampling methods such as Cisco's NetFlow are widely employed by large networks to reduce the amount of traffic data measured. A key problem with packet sampling is that it is inherently a lossy process, discarding (potentially useful) information. In this paper, we empirically evaluate the impact of sampling on anomaly detection metrics. Starting with unsampled flow records collected during the Blaster worm outbreak, we reconstruct the underlying packet trace and simulate packet sampling at increasing rates. We then use our knowledge of the Blaster anomaly to build a baseline of normal traffic (without Blaster), against which we can measure the anomaly size at various sampling rates. This approach allows us to evaluate the impact of packet sampling on anomaly detection without being restricted to (or biased by) a particular anomaly detection method.We find that packet sampling does not disturb the anomaly size when measured in volume metrics such as the number of bytes and number of packets, but grossly biases the number of flows. However, we find that recently proposed entropy-based summarizations of packet and flow counts are affected less by sampling, and expose the Blaster worm outbreak even at higher sampling rates. Our findings suggest that entropy summarizations are more resilient to sampling than volume metrics. Thus, while not perfect, sampling still preserves sufficient distributional structure, which when harnessed by tools like entropy, can expose hard-to-detect scanning anomalies.

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https://doi.org/10.1109/NOMS59830.2024.10575857
Hajj SAzar JBou Abdo JDemerjian JGuyeux CMakhoul AGinhac D(2023)Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection SystemsSensors10.3390/s2316703823:16(7038)Online publication date: 9-Aug-2023
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Index Terms

Impact of packet sampling on anomaly detection metrics
1. Networks
  1. Network components
  2. Network types
    1. Public Internet

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Published In

cover image ACM Conferences

IMC '06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement

October 2006

356 pages

ISBN:1595935614

DOI:10.1145/1177080

General Chairs:
Jussara Almeida
Federal University of Minas Gerais, Brazil
,
Virgilio Almeida
Federal University of Minas Gerais, Brazil
,
Program Chair:
Paul Barford
University of Wisconsin -- Madison, USA

Copyright © 2006 ACM.

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Publication History

Published: 25 October 2006

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October 25 - 27, 2006

Rio de Janeriro, Brazil

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

Kumar GLagesse B(2024)Thimblerig: A Game-Theoretic, Adaptive, Risk-limiting Security System for Cloud SystemsNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575857(1-6)Online publication date: 6-May-2024
https://doi.org/10.1109/NOMS59830.2024.10575857
Hajj SAzar JBou Abdo JDemerjian JGuyeux CMakhoul AGinhac D(2023)Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection SystemsSensors10.3390/s2316703823:16(7038)Online publication date: 9-Aug-2023
https://doi.org/10.3390/s23167038
Clausen HManocha AGibson M(2022)Detecting Proxies Relaying Streaming Internet TrafficIEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFOCOMWKSHPS54753.2022.9797963(1-6)Online publication date: 2-May-2022
https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9797963
Lai YTsai CChuang CKu XChen J(2022)Tabular Interpolation Approach Based on Stable Random Projection for Estimating Empirical Entropy of High-Speed Network TrafficIEEE Access10.1109/ACCESS.2022.321033610(104934-104953)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3210336
Novo CSilva JMorla R(2021)An Outlook on using Packet Sampling in Flow-based C2 TLS Malware Traffic Detection2021 12th International Conference on Network of the Future (NoF)10.1109/NoF52522.2021.9609889(1-5)Online publication date: 6-Oct-2021
https://doi.org/10.1109/NoF52522.2021.9609889
Hajj SSibai RAbdo JDemerjian JGuyeux CMakhoul AGinhac D(2021)A Critical Review on the Implementation of Static Data Sampling Techniques to Detect Network AttacksIEEE Access10.1109/ACCESS.2021.31186059(138903-138938)Online publication date: 2021
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Polverini MCianfrani AListanti M(2020)A Theoretical Framework for Network Monitoring Exploiting Segment Routing CountersIEEE Transactions on Network and Service Management10.1109/TNSM.2020.300180917:3(1924-1940)Online publication date: Sep-2020
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Qin TLiu ZWang PLi SGuan XGao L(2020)Symmetry Degree Measurement and its Applications to Anomaly DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.293373115(1040-1055)Online publication date: 2020
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Sibai RAbdo JChabchoub YDemerjian JChiky RBarbar K(2020)Data Summarization Using Sampling Algorithms: Data Stream Case StudyPrinciples of Data Science10.1007/978-3-030-43981-1_6(105-124)Online publication date: 9-Jul-2020
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Zhao KWang JQi HXie XZhou XLi K(2020)HBL-Sketch: A New Three-Tier Sketch for Accurate Network MeasurementAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-38991-8_4(48-59)Online publication date: 22-Jan-2020
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