TU Darmstadt / ULB / TUbiblio

Detecting a Crisis: Comparison of Self-Reported vs. Automated Internet Outage Measuring Methods

Orlov, Denis ; Möller, Simon ; Düfer, Sven ; Haesler, Steffen ; Reuter, Christian
Hrsg.: Marky, Karola ; Grünefeld, Uwe ; Kosch, Thomas (2022)
Detecting a Crisis: Comparison of Self-Reported vs. Automated Internet Outage Measuring Methods.
Mensch und Computer 2022: Facing Realities. Darmstadt, Germany (04.-07.09.2022)
doi: 10.18420/muc2022-mci-ws10-321
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Every day, there are internet disruptions or outages around the world that affect our daily lives. In this paper, we analyzed these events in Germany in recent years and found out how they can be detected, and what impact they have on citizens, especially in crisis situations. For this purpose, we take a look at two different approaches to recording internet outages, namely the self-reporting of citizens and automatic reporting by algorithmic examination of the availability of IP networks. We evaluate the data of six major events with regard to their meaningfulness in quality and quantity. We found that due to the amount of data and the inherent imprecision of the methods used, it is difficult to detect outages through algorithmic examination. But once an event is publicly known by self-reporting, they have advantages to capture the temporal and spatial dimensions of the outage due to its nature of objective measurements. As a result, we propose that users’ crowdsourcing can enhance the detection of outages and should be seen as an important starting point to even begin an analysis with algorithm-based techniques, but it is to ISPs and regulatory authorities to support that.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2022
Herausgeber: Marky, Karola ; Grünefeld, Uwe ; Kosch, Thomas
Autor(en): Orlov, Denis ; Möller, Simon ; Düfer, Sven ; Haesler, Steffen ; Reuter, Christian
Art des Eintrags: Bibliographie
Titel: Detecting a Crisis: Comparison of Self-Reported vs. Automated Internet Outage Measuring Methods
Sprache: Englisch
Publikationsjahr: 2022
Verlag: Gesellschaft für Informatik e.V.
Buchtitel: Mensch und Computer 2022 - Workshopband
Veranstaltungstitel: Mensch und Computer 2022: Facing Realities
Veranstaltungsort: Darmstadt, Germany
Veranstaltungsdatum: 04.-07.09.2022
DOI: 10.18420/muc2022-mci-ws10-321
URL / URN: https://dl.gi.de/handle/20.500.12116/39089
Kurzbeschreibung (Abstract):

Every day, there are internet disruptions or outages around the world that affect our daily lives. In this paper, we analyzed these events in Germany in recent years and found out how they can be detected, and what impact they have on citizens, especially in crisis situations. For this purpose, we take a look at two different approaches to recording internet outages, namely the self-reporting of citizens and automatic reporting by algorithmic examination of the availability of IP networks. We evaluate the data of six major events with regard to their meaningfulness in quality and quantity. We found that due to the amount of data and the inherent imprecision of the methods used, it is difficult to detect outages through algorithmic examination. But once an event is publicly known by self-reporting, they have advantages to capture the temporal and spatial dimensions of the outage due to its nature of objective measurements. As a result, we propose that users’ crowdsourcing can enhance the detection of outages and should be seen as an important starting point to even begin an analysis with algorithm-based techniques, but it is to ISPs and regulatory authorities to support that.

Freie Schlagworte: emergenCITY_SG; emergenCITY_INF
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Wissenschaft und Technik für Frieden und Sicherheit (PEASEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 26 Okt 2022 07:03
Letzte Änderung: 28 Mär 2024 13:46
PPN: 506241971
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