Conference Paper

Was bedeutet das IT-Sicherheitsgesetz für Smart Buildings? Konzepte des sicheren Alterns der Gebäude-IT

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Abstract

Nachdem das neue IT-Sicherheitsgesetz des Bundes in Kraft trat, bestehteine gegenwärtige rechtliche Verpflichtung vor allem für Betreiber sogenannter „kritischer Infrastrukturen“, innerhalb von zwei Jahren entsprechende IT-Sicherheitsmaßnahmen zu implementieren. Zu den kritischen Infrastrukturen zählen auch zahlreiche automatisierte Gebäude, die häufig IT-Sicherheitslücken aufweisen. In diesem Beitrag stellen wir unser Konzept des sicheren Alterns vor. Es basiert auf der Anwendung von maschinellen Lernverfahren zur Anomalieerkennung im BACnet-Netzwerkverkehr. Aus den Ergebnissen werden Entscheidungsregeln für das Filtern der Datenpakete abgeleitet. Damit wird ein selbstlernendes System erzeugt, das in der Lage ist, auch auf bisher unbekannte Angriffe angemessen zu reagieren. Unser Fokus liegt dabei auf der Evaluierung der Effektivität verschiedener maschineller Lernverfahren. Insbesondere zeigen wir, welche Methoden für die Erkennung bereits bekannter Anomalien sowie für die Entdeckung neuer Angriffe am besten geeignet sind.

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Deutscher Bundestag, "Entwurf eines Gesetzes zur Erhöhung der Sicherheit informationstechnischer Systeme." Drs. 18/4096, S. 26, 2015, [Online]. Available: http://dip21.bundestag.de/dip21/btd/18/040/1804096.pdf. [Zugriff am 7 Dezember 2016].
IT-Sicherheitsgesetz: Auswirkungen, Entwicklung und Materialien für die Praxis
  • M Terhaag
M. Terhaag, "IT-Sicherheitsgesetz: Auswirkungen, Entwicklung und Materialien für die Praxis." Bundesanzeiger Verlag, Auflage: 1, S. 46, 2015.
IoT Security Artifacts
  • B Moyer
B. Moyer, "IoT Security Artifacts." EEJournal, 2015, [Online]. Available: http://www.eejournal.com/archives/articles/20150824-security/. [Zugriff am 7 Dezember 2016].
IoT's Security Nightmare: Unpatched Devices that Never Die
  • A Noller
A. Noller, "IoT's Security Nightmare: Unpatched Devices that Never Die." DZone, 2014,[Online]. Available:https://dzone.com/articles/iots-security-nightmare. [Zugriff am 7 Dezember 2016].
Usable TRUST in the Internet of Things
  • Utrustit-Projekt
UTRUSTit-Projekt, "Usable TRUST in the Internet of Things," 2013, [Online].
Gartner Predicts our Digital Future
  • Inc Gartner
Gartner, Inc., "Gartner Predicts our Digital Future," 2015, [Online]. Available: https://www.gartner.com/smarterwithgartner/gartner-predicts-our-digital-future. [Zugriff am 7 Dezember 2016].
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  • J Kaur
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