Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Hybrid Data Mining to Reduce False Positive and False Negative Prediction in Intrusion Detection System Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Hybrid Anomaly Detection System for Electronic Control Units Featuring Replicator Neural Networks

Authors : Marc Weber, Felix Pistorius, Eric Sax, Jonas Maas, Bastian Zimmer

Published in: Advances in Information and Communication Networks

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Due to the steadily increasing connectivity combined with the trend towards autonomous driving, cyber security is essential for future vehicles. The implementation of an intrusion detection system (IDS) can be one building block in a security architecture. Since the electric and electronic (E/E) subsystem of a vehicle is fairly static, the usage of anomaly detection mechanisms within an IDS is promising. This paper introduces a hybrid anomaly detection system for embedded electronic control units (ECU), which combines the advantages of an efficient specification-based system with the advanced detection measures provided by machine learning. The system is presented for - but not limited to - the detection of anomalies in automotive Controller Area Network (CAN) communication. The second part of this paper focuses on the machine learning aspect of the proposed system. The usage of Replicator Neural Networks (RNN) to detect anomalies in the time series of CAN signals is investigated in more detail. After introducing the working principle of RNNs, the application of this algorithm on time series data is presented. Finally, first evaluation results of a prototypical implementation are discussed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Open Algorithms for Identity Federation
next chapter Optimizing Noise Level for Perturbing Geo-location Data
Literature
1.
Miller, C., Valasek, C.: A survey of remote automotive attack surfaces. In: Black Hat USA, vol. 2014 (2014)
2.
Miller, C., Valasek, C.: Remote exploitation of an unaltered passenger vehicle. In: Black Hat USA, vol. 2015 (2015)
3.
Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., Kohno, T., et al.: Comprehensive experimental analyses of automotive attack surfaces. In: USENIX Security Symposium (2011)
4.
Onishi, H.: Paradigm change of vehicle cyber security. In: Czosseck, C. (ed.) 4th International Conference on Cyber Conflict (CYCON). IEEE, Piscataway (2012)
5.
AUTO-ISAC: Automotive Cybersecurity Best Practices: Executive Summary. In: AUTO-ISAC, 2016th edn. (2016)
6.
Brown, D.A., Cooper, G., Gilvarry, I., Grawrock, D., Rajan, A., Tatourian, A., Venugopalan, R., Vishik, C., Wheeler, D., Zhao, M., Clare, D., Fry, S., Handschuh, H., Patil, H., Poulin, C., Wasicek, A., Wood, R.: Automotive security best practices: recommendations for security and privacy in the era of the next-generation car. In: White Paper, McAfee Inc. (2015)
7.
van Roermund, T., Birnie, A.: A multi-layer vehicle security framework. In: Whitepaper, May 2016 ed. NXP B.V. (2016)
8.
Hoppe, T., Kiltz, S., Dittmann, J.: Security threats to automotive can networks–practical examples and selected short-term countermeasures. Reliab. Eng. Syst. Saf. 96(1), 11–25 (2011)CrossRef
9.
Studnia, I., Nicomette, V., Alata, E., Deswarte, Y., Kaâniche, M., Laarouchi, Y.: Security of embedded automotive networks: state of the art and a research proposal. In: Roy, M. (ed.) SAFECOMP 2013 - Workshop CARS (2nd Workshop on Critical Automotive Applications: Robustness & Safety) of the 32nd International Conference on Computer Safety, Reliability and Security, France, Toulouse (2013)
10.
Hawkins, D.M.: Identification of Outliers, Monographs on Applied Probability and Statistics. Springer, Dordrecht (1980)
11.
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)CrossRef
12.
Theissler, A.: Anomaly detection in recordings from in-vehicle networks. In: Proceedings of Big Data Applications and Principles, Madrid (2014)
13.
Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Arikawa, M., Winiwarter, W. (eds.) Data Warehousing and Knowledge Discovery, Lecture Notes in Computer Science. Springer, Heidelberg (2002)
14.
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, vol. 12, pp. 582–588. MIT Press, Cambridge (2000)
15.
Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst. 123, 163–173 (2017)CrossRef
16.
Williams, G., Baxter, R., He, H., Hawkins, S., Gu, L.: A comparative study of rnn for outlier detection in data mining. In: Kumar, V. (ed.) Proceedings/2002 IEEE International Conference on Data Mining, ICDM 2002, pp. 709–712. IEEE Computer Society, Los Alamitos (2002)
17.
Minsky, M.L., Papert, S.A.: Perceptrons: An Introduction to Computational Geometry, 2nd edn. The MIT Press, Cambridge (1972)MATH
18.
Dau, H.A., Ciesielski, V., Song, A.: Anomaly detection using replicator neural networks trained on examples of one class. In: Dick, G. (ed.) Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 8886, pp. 311–322. Springer, Cham (2014)
19.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing, A Bradford Book, pp. 318–362. MIT Press, Cambridge (1986)
20.
Fernández-Redondo, M., Hernández-Espinosa, C.: Weight initialization methods for multilayer feedforward. In: Verleysen, M. (ed.) Proceedings/9th European Symposium on Artificial Neural Networks, ESANN 2001, pp. 119–124. D-Facto, Brussels (2001)
Metadata
Title
A Hybrid Anomaly Detection System for Electronic Control Units Featuring Replicator Neural Networks
Authors
Marc Weber
Felix Pistorius
Eric Sax
Jonas Maas
Bastian Zimmer
Copyright Year
2019
Book
Advances in Information and Communication Networks

Print ISBN: 978-3-030-03404-7

Electronic ISBN: 978-3-030-03405-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-03405-4_4