Skip to main content

2015 | OriginalPaper | Buchkapitel

Intrusion Detection System for Applications Using Linux Containers

verfasst von : Amr S. Abed, Charles Clancy, David S. Levy

Erschienen in: Security and Trust Management

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Linux containers are gaining increasing traction in both individual and industrial use, and as these containers get integrated into mission-critical systems, real-time detection of malicious cyber attacks becomes a critical operational requirement. This paper introduces a real-time host-based intrusion detection system that can be used to passively detect malfeasance against applications within Linux containers running in a standalone or in a cloud multi-tenancy environment. The demonstrated intrusion detection system uses bags of system calls monitored from the host kernel for learning the behavior of an application running within a Linux container and determining anomalous container behavior. Performance of the approach using a database application was measured and results are discussed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

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!

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!

Literatur
1.
Zurück zum Zitat Alarifi, S., Wolthusen, S.: Detecting anomalies in IaaS environments through virtual machine host system call analysis. In: International Conference for Internet Technology and Secured Transactions, pp. 211–218. IEEE (2012) Alarifi, S., Wolthusen, S.: Detecting anomalies in IaaS environments through virtual machine host system call analysis. In: International Conference for Internet Technology and Secured Transactions, pp. 211–218. IEEE (2012)
2.
Zurück zum Zitat Alarifi, S., Wolthusen, S.: Anomaly detection for ephemeral cloud IaaS virtual machines. In: Lopez, J., Huang, X., Sandhu, R. (eds.) NSS 2013. LNCS, vol. 7873, pp. 321–335. Springer, Heidelberg (2013) CrossRef Alarifi, S., Wolthusen, S.: Anomaly detection for ephemeral cloud IaaS virtual machines. In: Lopez, J., Huang, X., Sandhu, R. (eds.) NSS 2013. LNCS, vol. 7873, pp. 321–335. Springer, Heidelberg (2013) CrossRef
3.
Zurück zum Zitat Chen, Y., Ghorbanzadeh, M., Ma, K., Clancy, C., McGwier, R.: A hidden markov model detection of malicious android applications at runtime. In: 2014 23rd Wireless and Optical Communication Conference (WOCC), pp. 1–6, May 2014 Chen, Y., Ghorbanzadeh, M., Ma, K., Clancy, C., McGwier, R.: A hidden markov model detection of malicious android applications at runtime. In: 2014 23rd Wireless and Optical Communication Conference (WOCC), pp. 1–6, May 2014
4.
Zurück zum Zitat Cho, S.B., Park, H.J.: Efficient anomaly detection by modeling privilege flows using hidden markov model. Comput. Secur. 22(1), 45–55 (2003)CrossRef Cho, S.B., Park, H.J.: Efficient anomaly detection by modeling privilege flows using hidden markov model. Comput. Secur. 22(1), 45–55 (2003)CrossRef
5.
Zurück zum Zitat Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California (1995) Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California (1995)
7.
Zurück zum Zitat Forrest, S., Hofmeyr, S., Somayaji, A., Longstaff, T.: A sense of self for unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 120–128, May 1996 Forrest, S., Hofmeyr, S., Somayaji, A., Longstaff, T.: A sense of self for unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 120–128, May 1996
8.
Zurück zum Zitat Fuller, D., Honavar, V.: Learning classifiers for misuse and anomaly detection using a bag of system calls representation. In: Proceedings of the Sixth Annual IEEE Systems, Man and Cybernetics (SMC) Information Assurance Workshop, pp. 118–125. IEEE (2005) Fuller, D., Honavar, V.: Learning classifiers for misuse and anomaly detection using a bag of system calls representation. In: Proceedings of the Sixth Annual IEEE Systems, Man and Cybernetics (SMC) Information Assurance Workshop, pp. 118–125. IEEE (2005)
9.
Zurück zum Zitat Helsley, M.: LXC: Linux container tools. IBM developerWorks Technical Library (2009) Helsley, M.: LXC: Linux container tools. IBM developerWorks Technical Library (2009)
10.
Zurück zum Zitat Hoang, X.D., Hu, J., Bertok, P.: A multi-layer model for anomaly intrusion detection using program sequences of system calls. In: Proceedings of the 11th IEEE International Conference on Networks, pp. 531–536 (2003) Hoang, X.D., Hu, J., Bertok, P.: A multi-layer model for anomaly intrusion detection using program sequences of system calls. In: Proceedings of the 11th IEEE International Conference on Networks, pp. 531–536 (2003)
11.
Zurück zum Zitat Hofmeyr, S., Forrest, S., Somayaji, A.: Intrusion detection using sequences of system calls. J. Comput. Secur. 6(3), 151–180 (1998)CrossRef Hofmeyr, S., Forrest, S., Somayaji, A.: Intrusion detection using sequences of system calls. J. Comput. Secur. 6(3), 151–180 (1998)CrossRef
12.
Zurück zum Zitat Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Usenix Security (1998) Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: Usenix Security (1998)
13.
Zurück zum Zitat Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014) Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)
14.
Zurück zum Zitat Murtaza, S.S., Khreich, W., Hamou-Lhadj, A., Couture, M.: A host-based anomaly detection approach by representing system calls as states of kernel modules. In: 2013 IEEE 24th International Symposium onSoftware Reliability Engineering (ISSRE), pp. 431–440. IEEE (2013) Murtaza, S.S., Khreich, W., Hamou-Lhadj, A., Couture, M.: A host-based anomaly detection approach by representing system calls as states of kernel modules. In: 2013 IEEE 24th International Symposium onSoftware Reliability Engineering (ISSRE), pp. 431–440. IEEE (2013)
15.
Zurück zum Zitat Mutz, D., Valeur, F., Vigna, G., Kruegel, C.: Anomalous system call detection. ACM Trans. Inf. Syst. Secur. (TISSEC) 9(1), 61–93 (2006)CrossRef Mutz, D., Valeur, F., Vigna, G., Kruegel, C.: Anomalous system call detection. ACM Trans. Inf. Syst. Secur. (TISSEC) 9(1), 61–93 (2006)CrossRef
18.
Zurück zum Zitat Wang, W., Guan, X.H., Zhang, X.L.: Modeling program behaviors by hidden markov models for intrusion detection. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2830–2835. IEEE (2004) Wang, W., Guan, X.H., Zhang, X.L.: Modeling program behaviors by hidden markov models for intrusion detection. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2830–2835. IEEE (2004)
19.
Zurück zum Zitat Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: alternative data models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 133–145 (1999) Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: alternative data models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 133–145 (1999)
20.
Zurück zum Zitat Yeung, D.Y., Ding, Y.: Host-based intrusion detection using dynamic and static behavioral models. Pattern Recogn. 36(1), 229–243 (2003)CrossRefMATH Yeung, D.Y., Ding, Y.: Host-based intrusion detection using dynamic and static behavioral models. Pattern Recogn. 36(1), 229–243 (2003)CrossRefMATH
Metadaten
Titel
Intrusion Detection System for Applications Using Linux Containers
verfasst von
Amr S. Abed
Charles Clancy
David S. Levy
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-24858-5_8