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Using a Deep Understanding of Network Activities for Network Vulnerability Assessment

Mona Lange, Felix Kuhr, Ralf Möller

Abstract

In data-communication networks, network reliability is of great concern to both network operators and customers. Therefore, network operators want to determine what services could be affected by software vulnerabilities being exploited that are present within their data-communication network. To determine what services could be affected by a software vulnerability being exploited, it is fundamentally important to know the ongoing tasks in a network. A particular task may depend on multiple network services, spanning many network devices. Unfortunately, dependency details are often not documented and are difficult to discover by relying on human expert knowledge. In monitored networks huge amounts of data are available and by applying data mining techniques, we are able to extract information of ongoing network activities. From a data mining perspective, we are interested to test the potential of applying data mining techniques to real-life applications. © 2016 The Authors and IOS Press.

OriginalspracheEnglisch
TitelProceedings of the 1st International Workshop on AI for Privacy and Security, PrAISe@ECAI 2016, The Hague, Netherlands,29.08.-02.09.
Seitenumfang3
Band285
Herausgeber (Verlag)IOS Press
Erscheinungsdatum01.08.2016
Seiten1583 - 1585
ISBN (Print)978-1-4503-4304-6
DOIs
PublikationsstatusVeröffentlicht - 01.08.2016
Veranstaltung22nd European Conference on Artificial Intelligence
- The Hague, Niederlande
Dauer: 29.08.201602.09.2016
Konferenznummer: 126200

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 3 – Gesundheit und Wohlergehen
    SDG 3 – Gesundheit und Wohlergehen
  2. SDG 9 – Industrie, Innovation und Infrastruktur
    SDG 9 – Industrie, Innovation und Infrastruktur

DFG-Fachsystematik

  • 409-06 Informationssysteme, Prozess- und Wissensmanagement

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