research-article

ABSENCE: Usage-based Failure Detection in Mobile Networks

Authors:

Jacobus Van der Merwe,

Jennifer YatesAuthors Info & Claims

MobiCom '15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

Pages 464 - 476

https://doi.org/10.1145/2789168.2790127

Published: 07 September 2015 Publication History

Abstract

We present our proposed ABSENCE system which detects service disruptions in mobile networks using aggregated customer usage data. ABSENCE monitors aggregated customer usage to detect when aggregated usage is lower than expected in a given geographic region (e.g., zip code), across a given customer device type, or for a given service. Such a drop in expected usage is interpreted as a sign of a potential service disruption being experienced in that region/device type/service. ABSENCE effectively deals with users' mobility and scales to detect failures in various mobile services (e.g., voice, data, SMS, MMS, etc). We perform a systematic evaluation of our proposed approach by introducing synthetic failures in measurements obtained from a US operator. We also compare our results with ground truth (real service disruptions) obtained from the mobile operator.

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Cited By

Rong GWang HGu SXu YSun JShao DZhang H(2023)Locating Anomaly Clues for Atypical Anomalous Services: An Industrial ExplorationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.318114320:4(2746-2761)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TDSC.2022.3181143
Hammad MHewahi NElmedany W(2023)Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approachArab Journal of Basic and Applied Sciences10.1080/25765299.2023.226121930:1(561-572)Online publication date: 25-Sep-2023
https://doi.org/10.1080/25765299.2023.2261219
Han JLiu TMa JZhou YZeng XXu Y(2022)Anomaly Detection and Early Warning Model for Latency in Private 5G NetworksApplied Sciences10.3390/app12231247212:23(12472)Online publication date: 6-Dec-2022
https://doi.org/10.3390/app122312472
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Index Terms

ABSENCE: Usage-based Failure Detection in Mobile Networks
1. Networks
  1. Network services
    1. Network management
    2. Network monitoring

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cover image ACM Conferences

MobiCom '15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

September 2015

638 pages

ISBN:9781450336192

DOI:10.1145/2789168

General Chairs:
Serge Fdida
UPMC/LIP6, France
,
Giovanni Pau
UPMC/LIP6, France
,
Program Chairs:
Sneha Kumar Kasera
University of Utah, USA
,
Heather Zheng
University of California, Santa Barbara, USA

Copyright © 2015 ACM.

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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

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National Science Foundation

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MobiCom'15

Sponsor:

SIGMOBILE

MobiCom'15: The 21th Annual International Conference on Mobile Computing and Networking

September 7 - 11, 2015

Paris, France

Acceptance Rates

MobiCom '15 Paper Acceptance Rate 38 of 207 submissions, 18%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

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https://doi.org/10.1109/TDSC.2022.3181143
Hammad MHewahi NElmedany W(2023)Enhancing Network Intrusion Recovery in SDN with machine learning: an innovative approachArab Journal of Basic and Applied Sciences10.1080/25765299.2023.226121930:1(561-572)Online publication date: 25-Sep-2023
https://doi.org/10.1080/25765299.2023.2261219
Han JLiu TMa JZhou YZeng XXu Y(2022)Anomaly Detection and Early Warning Model for Latency in Private 5G NetworksApplied Sciences10.3390/app12231247212:23(12472)Online publication date: 6-Dec-2022
https://doi.org/10.3390/app122312472
Chakor AMonir AAbdellah A(2022)Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology SupervisionApplied Computer Systems10.2478/acss-2022-000527:1(43-54)Online publication date: 23-Aug-2022
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Kilinc CMarina MUsama MErgut SCrowcroft JGundogdu TAkinci I(2022)JADE: Data-Driven Automated Jammer Detection Framework for Operational Mobile NetworksIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796674(1139-1148)Online publication date: 2-May-2022
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Manousis AShah HMilner HLi YZhang HSekar VLevin DMislove AAmann JLuckie M(2021)The shape of viewProceedings of the 21st ACM Internet Measurement Conference10.1145/3487552.3487819(245-260)Online publication date: 2-Nov-2021
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Mendoza CMcgarry M(2020)The Network Link Outlier Factor (NLOF) for Fault LocalizationIEEE Open Journal of the Communications Society10.1109/OJCOMS.2020.30256631(1539-1550)Online publication date: 2020
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