Article

Characteristics of network traffic flow anomalies

Authors:

Paul Barford,

David PlonkaAuthors Info & Claims

IMW '01: Proceedings of the 1st ACM SIGCOMM Workshop on Internet measurement

Pages 69 - 73

https://doi.org/10.1145/505202.505211

Published: 01 November 2001 Publication History

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Cited By

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Xu KWan YLin XWang FXue G(2023)Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00052(366-374)Online publication date: 25-Sep-2023
https://doi.org/10.1109/MASS58611.2023.00052
Gu KWu JJiang FJi RJi LLei T(2023)Anomaly Detection with Ensemble Empirical Mode Decomposition for Secure QUIC Communications: A Simple Use CaseMobile Networks and Management10.1007/978-3-031-32443-7_30(413-422)Online publication date: 28-May-2023
https://doi.org/10.1007/978-3-031-32443-7_30
Machoke MMbelwa JAgbinya JSam A(2022)Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic FlowEngineering, Technology & Applied Science Research10.48084/etasr.485212:3(8667-8674)Online publication date: 6-Jun-2022
https://doi.org/10.48084/etasr.4852
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Index Terms

Characteristics of network traffic flow anomalies
1. Networks
  1. Network protocols
    1. Protocol correctness
      1. Protocol testing and verification
  2. Network services
    1. Network monitoring

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Information & Contributors

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Published In

IMW '01: Proceedings of the 1st ACM SIGCOMM Workshop on Internet measurement

November 2001

319 pages

ISBN:1581134355

DOI:10.1145/505202

Conference Chair:
Vern Paxson
ACIRI

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2001

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Conference

SIGCOMM WS'01

Sponsor:

SIGCOMM

SIGCOMM WS'01: ACM SIGCOMM Internet Measurement Workshop 2001

November 1 - 2, 2001

California, San Francisco, USA

Acceptance Rates

IMW '01 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 29 of 80 submissions, 36%

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Cited By

View all

Xu KWan YLin XWang FXue G(2023)Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS58611.2023.00052(366-374)Online publication date: 25-Sep-2023
https://doi.org/10.1109/MASS58611.2023.00052
Gu KWu JJiang FJi RJi LLei T(2023)Anomaly Detection with Ensemble Empirical Mode Decomposition for Secure QUIC Communications: A Simple Use CaseMobile Networks and Management10.1007/978-3-031-32443-7_30(413-422)Online publication date: 28-May-2023
https://doi.org/10.1007/978-3-031-32443-7_30
Machoke MMbelwa JAgbinya JSam A(2022)Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic FlowEngineering, Technology & Applied Science Research10.48084/etasr.485212:3(8667-8674)Online publication date: 6-Jun-2022
https://doi.org/10.48084/etasr.4852
Jarabo-Penas AZufiria PGarcia-Maurino C(2022)Bus Headways Analysis for Anomaly DetectionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.315518023:10(18975-18988)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3155180
S SV AG AM B(2022)RPCA and Wavelet packet Decomposition based Network Traffic Anomaly Detection2022 IEEE 19th India Council International Conference (INDICON)10.1109/INDICON56171.2022.10039837(1-5)Online publication date: 24-Nov-2022
https://doi.org/10.1109/INDICON56171.2022.10039837
V JK MG GD D(2022)DLMHS: Flow‐based intrusion detection system using deep learning neural network and meta‐heuristic scaleInternational Journal of Communication Systems10.1002/dac.515935:10Online publication date: 5-Apr-2022
https://doi.org/10.1002/dac.5159
Frikha MGammar SLahmadi A(2021)Multi-Attribute Monitoring for Anomaly Detection: a Reinforcement Learning Approach based on Unsupervised Reward2021 10th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired Networks (PEMWN)10.23919/PEMWN53042.2021.9664667(1-6)Online publication date: 23-Nov-2021
https://doi.org/10.23919/PEMWN53042.2021.9664667
Perdices DGarcia-Dorado JRamos JDe Pool RAracil J(2021)Towards the Automatic and Schedule-Aware Alerting of Internetwork Time SeriesIEEE Access10.1109/ACCESS.2021.30735989(61346-61358)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3073598
Turcato ANegri LDias ASestito GFlauzino R(2021)A Cloud-Based Method for Detecting Intrusions in PROFINET Communication Networks Based on Anomaly DetectionJournal of Control, Automation and Electrical Systems10.1007/s40313-021-00747-432:5(1177-1188)Online publication date: 22-Jun-2021
https://doi.org/10.1007/s40313-021-00747-4
Machaka PBagula A(2021)Statistical Properties and Modelling of DDoS AttacksContext-Aware Systems and Applications, and Nature of Computation and Communication10.1007/978-3-030-67101-3_4(44-54)Online publication date: 13-Jan-2021
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