Vortrag
  • Karl Leidl
  • R. Habermann
Intelligente Anlagenüberwachung - Digitalisierung sicher meistern

In: MES im Fokus

  • 2020
  • Institut ProtectIT
  • DIGITAL
Vortrag
  • Karl Leidl
Anomalieerkennung in industriellen Netzwerken - Cybersicherheit mit Machine Learning

In: Forum Künstliche Intelligenz

  • 2019
  • TC Teisnach Sensorik
  • Institut ProtectIT
  • Elektrotechnik und Medientechnik
  • DIGITAL
Zeitschriftenartikel
  • Karl Leidl
  • Andreas Grzemba
Secure per Machine Learning - Wie KI die Informationssicherheit verbessern kann

In: Computer & Automation (Sonderheft Safety & Security)

  • 2019

  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)
  • Karl Leidl
  • Andreas Grzemba
Cybersicherheit in industriellen Netzwerken - Intrusion Detection mit Machine Learning
  • 2019
  • Institut ProtectIT
  • Elektrotechnik und Medientechnik
  • TC Teisnach Sensorik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)
  • Robert Wildenauer
  • Karl Leidl
  • Martin Schramm
Hacking an optics manufacturing machine: You don't see it coming?!, pg. 11171071-11171076.
  • 2019

DOI: 10.1117/12.2526691

With more and more industrial devices getting inter-connected the attack surface for cyber attacks is increasing steadily. In this paper the possible approach of an attacker who got access to the office network at the Institute for Precision Manufacturing and High-Frequency Technology (IPH) to attack one of the optic machines that reside in another network segment is presented. Based on known vulnerabilities from the Common Vulnerabilities and Exposures (CVE), like the shellshock exploit or remote code execution with PsExec, for devices identified in the network, an attacker can bypass the firewall between the office network and the laboratory network and get full access to the HMI of the target machine.
  • Institut ProtectIT
  • Elektrotechnik und Medientechnik
  • TC Teisnach Sensorik
  • DIGITAL
Vortrag
  • Karl Leidl
Cybersicherheit in industriellen Netzwerken - Intrusion Detection mit Machine Learning

In: Forum Safety & Security

  • 2019
  • Elektrotechnik und Medientechnik
  • TC Teisnach Sensorik
  • Institut ProtectIT
  • DIGITAL
Zeitschriftenartikel
  • Nari Arunraj
  • Robert Hable
  • Michael Fernandes
  • Karl Leidl
  • Michael Heigl
Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection Systems (NIDS) Application, pg. 10-19.

In: Anwendungen und Konzepte in der Wirtschaftsinformatik (AKWI)

  • 2017
With the emergence of the fourth industrial revolution (Industrie 4.0) of cyber physical systems, intrusion detection systems are highly necessary to detect industrial network attacks. Recently, the increase in application of specialized machine learning techniques is gaining critical attention in the intrusion detection community. A wide variety of learning techniques proposed for different network intrusion detection system (NIDS) problems can be roughly classified into three broad categories: supervised, semi-supervised and unsupervised. In this paper, a comparative study of selected learning methods from each of these three kinds is carried out. In order to assess these learning methods, they are subjected to investigate network traffic datasets from an Airplane Cabin Demonstrator. In addition to this, the imbalanced classes (normal and anomaly classes) that are present in the captured network traffic data is one of the most crucial issues to be taken into consideration. From this investigation, it has been identified that supervised learning methods (logistic and lasso logistic regression methods) perform better than other methodswhen historical data on former attacks are available. The results of this study have also showed that the performance of semi-supervised learning method (One class support vector machine) is comparatively better than unsupervised learning method (Isolation Forest) when historical data on former attacks are not available.
  • Institut ProtectIT
  • TC Grafenau
  • TC Teisnach Sensorik
  • DIGITAL
Vortrag
  • Karl Leidl
  • Martin Aman
  • Michael Heigl
  • Andreas Grzemba
Intrusion Detection Sensoren für industrielle Netzwerke

In: CYBICS - Cyber Security for Industrial Control Systems (Workshop & Konferenz für IT-Sicherheit in der Industrie)

  • 2016
  • Elektrotechnik und Medientechnik
  • Institut ProtectIT
  • TC Teisnach Sensorik
Vortrag
  • Peter Semmelbauer
  • Karl Leidl
  • Martin Aman
  • Laurin Dörr
  • Andreas Grzemba
Schwachstellen, Angriffsszenarien und Schutzmaßnahmen bei industriellen Protokollen am Beispiel Profinet IO

In: Automation 2016 - Secure & reliable in the digital world

  • 2016
  • Elektrotechnik und Medientechnik
  • TC Teisnach Sensorik
  • Institut ProtectIT
Vortrag
  • Karl Leidl
Cyber Security for Process Control Networks

In: 1st European Seminar on Precision Optics Manufacturing

  • 2014
  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
Vortrag
  • Karl Leidl
  • Andreas Grzemba
  • Laurin Dörr
Live Hacking

In: it-sa

  • 2013
  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
Vortrag
  • Karl Leidl
Cyber Security for Industrial Control Systems

In: IHS Industrial Automation Conference

  • 2013
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
  • Institut ProtectIT
Vortrag
  • Karl Leidl
  • Peter Fröhlich
  • Andreas Grzemba
Embedded Security with Respect to Industrial Control Systems. Workshop

In: Embedded World Conference 2013

  • 2013
  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
Vortrag
  • Martin Schramm
  • Karl Leidl
  • Andreas Grzemba
  • N. Kuntze
Enhanced Embedded Device Security by Combining Hardware-Based Trust Mechanisms. Poster-Session

In: ACM Conference on Computer and Communications Security

  • 2013
  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik
Vortrag
  • Karl Leidl
  • Martin Schramm
  • Andreas Grzemba
The Establishment of High Degrees of Trust in a Linux Environment

In: Embedded World International Conference 2012

  • 2012
  • Institut ProtectIT
  • TC Teisnach Sensorik
  • Elektrotechnik und Medientechnik