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System Calls Instrumentation for Intrusion Detection in Embedded Mixed-Criticality Systems

Authors Marine Kadar, Sergey Tverdyshev, Gerhard Fohler

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Subject Classification

ACM Subject Classification
  • Security and privacy → Embedded systems security
  • Security and privacy → Intrusion detection systems
Keywords
  • Instrumentation
  • Mixed-criticality
  • Real-Time
  • System Calls
  • Host Intrusion Detection Systems

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Abstract

System call relative information such as occurrences, type, parameters, and return values are well established metrics to reveal intrusions in a system software. Many Host Intrusion Detection Systems (HIDS) from research and industry analyze these data for continuous system monitoring at runtime. Despite a significant false alarm rate, this type of defense offers high detection precision for both known and zero-day attacks. Recent research focuses on HIDS deployment for desktop computers. Yet, the integration of such run-time monitoring solution in mixed-criticality embedded systems has not been discussed. Because of the cohabitation of potentially vulnerable non-critical software with critical software, securing mixed-criticality systems is a non trivial but essential issue. Thus, we propose a methodology to evaluate the impact of deploying system call instrumentation in such context. We analyze the impact in a concrete use-case with PikeOS real-time hypervisor.

Cite As Get BibTex

Marine Kadar, Sergey Tverdyshev, and Gerhard Fohler. System Calls Instrumentation for Intrusion Detection in Embedded Mixed-Criticality Systems. In 4th International Workshop on Security and Dependability of Critical Embedded Real-Time Systems (CERTS 2019). Open Access Series in Informatics (OASIcs), Volume 73, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.CERTS.2019.2

Author Details

Marine Kadar
  • SYSGO GmbH, Klein-Winternheim, Germany
Sergey Tverdyshev
  • SYSGO GmbH, Klein-Winternheim, Germany
Gerhard Fohler
  • Technische Universität Kaiserslautern, Germany

Funding

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764785, FORA-Fog Computing for Robotics and Industrial Automation.

Acknowledgements

The authors would like to thank the anonymous reviewers of the paper at CERTS and internal reviewers at SYSGO for their valuable feedback.

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