A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques

Cyber-Physical Systems (CPS) are a logical step towards integrating classical IT systems further into physical or virtual surroundings. In consequence this means that CPS will be targets for new security threats, e.g. by manipulating the system both at the IT system level and within its surroundings. In this paper, we first discuss these new types of security threats before suggesting a novel system architecture that extends ideas from the domain of Organic Computing. Finally, we present a research agenda towards building future secure CPS.

[1]  Alexander Hofmann,et al.  Online Intrusion Alert Aggregation with Generative Data Stream Modeling , 2011, IEEE Transactions on Dependable and Secure Computing.

[2]  Takao Terano,et al.  Learning Classifier Systems Meet Multiagent Environments , 2000, IWLCS.

[3]  Jörg Hähner,et al.  Organic traffic light control for urban road networks , 2009, Int. J. Auton. Adapt. Commun. Syst..

[4]  Edward A. Lee.  Cyber-physical Systems -are Computing Foundations Adequate? Position Paper for Nsf Workshop on Cyber-physical Systems: Research Motivation, Techniques and Roadmap , 1998 .

[5]  Graham J. Williams,et al.  On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms , 2000, KDD '00.

[6]  Hartmut Schmeck,et al.  Collaborating and Learning Predators on a Pursuit Scenario , 2010, DIPES/BICC.

[7]  Seppo J. Ovaska,et al.  In Your Interest - Objective Interestingness Measures for a Generative Classifier , 2011, ICAART.

[8]  Bernhard Sick,et al.  Techniques for knowledge acquisition in dynamically changing environments , 2012, TAAS.

[9]  Melanie Volkamer,et al.  Determine the Resilience of Evaluated Internet Voting Systems , 2009, 2009 First International Workshop on Requirements Engineering for e-Voting Systems.

[10]  R. Solé,et al.  Information Theory of Complex Networks: On Evolution and Architectural Constraints , 2004 .

[11]  Hartmut Schmeck,et al.  Improving XCS Performance by Distribution , 2008, SEAL.

[12]  S. Shankar Sastry,et al.  Secure Control: Towards Survivable Cyber-Physical Systems , 2008, 2008 The 28th International Conference on Distributed Computing Systems Workshops.

[13]  A. Zell,et al.  Novelty Detection and Online Learning for Vibration-based Terrain Classification , 2022 .

[14]  Jörg Hähner,et al.  Observation and Control of Organic Systems , 2011, Organic Computing.

[15]  Jörg Hähner,et al.  Distributed Network Protocol Parameter Adaptation in Mobile Ad-Hoc Networks , 2010, ICINCO.

[16]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[17]  Hartmut Schmeck,et al.  Adaption of XCS to multi-learner predator/prey scenarios , 2010, GECCO '10.

[18]  Christian Müller-Schloer,et al.  Organic computing: on the feasibility of controlled emergence , 2004, CODES+ISSS '04.

[19]  Sven Tomforde.  An architectural framework for self-configuration and self-improvement at runtime , 2011 .

[20]  Martin Lauer,et al.  A Mixture Approach to Novelty Detection Using Training Data with Outliers , 2001, ECML.

[21]  Markus G. Kuhn,et al.  Tamper resistance: a cautionary note , 1996 .

[22]  Bernhard Sick,et al.  Learning from others: Exchange of classification rules in intelligent distributed systems , 2012, Artif. Intell..

[23]  Atta Badii,et al.  Security, trust and privacy supported by context-aware middleware , 2007 .

[24]  Magnus Jändel.  Cooperating Classifiers , 2008, NICSO.

[25]  Bruno Sinopoli,et al.  Challenges for Securing Cyber Physical Systems , 2009 .

[26]  Daniel P. Thunnissen,et al.  Uncertainty Classification for the Design and Development of Complex Systems , 2003 .

[27]  O. Weck,et al.  Series ESD-WP-2007-10 A CLASSIFICATION OF UNCERTAINTY FOR EARLY PRODUCT AND SYSTEM DESIGN , 2007 .

[28]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[29]  Aoying Zhou,et al.  Distributed Data Stream Clustering: A Fast EM-based Approach , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[30]  Christopher L. Magee,et al.  Complex System Classification , 2004 .

[31]  Nina Oertel.  Taxonomy development in information systems: Developing a taxonomy of mobile applications , 2009 .

[32]  Arno Wacker,et al.  A new approach for establishing pairwise keys for securing wireless sensor networks , 2005, SenSys '05.

[33]  Hartmut Schmeck,et al.  Organic Computing - A Paradigm Shift for Complex Systems , 2011, Organic Computing.

[34]  Christine M. Anderson-Cook,et al.  Book review: quantitative risk management: concepts, techniques and tools, revised edition, by A.F. McNeil, R. Frey and P. Embrechts. Princeton University Press, 2015, ISBN 978-0-691-16627-8, xix + 700 pp. , 2017, Extremes.

[35]  Michal Pechoucek,et al.  Collaborative Learning with Logic-Based Models , 2007, Adaptive Agents and Multi-Agents Systems.

[36]  Justus H. Piater,et al.  Online Learning of Gaussian Mixture Models - a Two-Level Approach , 2008, VISAPP.

[37]  Christian Müller-Schloer,et al.  Quantitative Emergence -- A Refined Approach Based on Divergence Measures , 2010, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[38]  R. Nelsen.  An Introduction to Copulas , 1998 .

[39]  Siu-Ming Yiu,et al.  Security Issues and Challenges for Cyber Physical System , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[40]  Gregor Schiele,et al.  Towards an authentication service for Peer-to-Peer based Massively Multiuser Virtual Environments , 2008, Int. J. Adv. Media Commun..