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Intrusion Detection in Software Defined Networks with Self-organized Maps

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The Software Defined Network (SDN) architecture provides new opportunities to implement security mechanisms in terms of unauthorized activities detection. At the same time, there are certain risks associated with this technology. The presented approach covers a conception of the measurement method, virtual testbed and classification mechanism for SDNs. The paper presents a measurement method which allows collecting network traffic flow parameters, generated by a virtual SDN environment. The collected dataset can be used in machine learning methods to detect unauthorized activities.
Rocznik
Tom
Strony
3--9
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Institute of Telecommunication, Faculty of Electronics, Military University of Technology, Warsaw, Poland
autor
  • Institute of Telecommunication, Faculty of Electronics, Military University of Technology, Warsaw, Poland
Bibliografia
  • [1] J. Kleban and M. Puciński, "Sieci sterowane programowo SDN w centrach danych SDDC" (SDN network software controlled data centers SDDC), in XVII Poznań Commun. Worksh. PWT 2013, Poznań, Poland, 2013 (in Polish).
  • [2] D. Kreutz et al., "Software Defined Networking: A Comprehensive Survey", Proc. of the IEEE, vol. 103, no. 1, pp. 14-76, 2015.
  • [3] S. Shin and G. Gu, "Attacking software-defined networks: A first feasibility study", in Proc. 2nd ACM SIGCOMM Worksh. Hot Topics in Softw. Def. Netw. HotSDN'13, Hong Kong, China, 2013, pp. 165-166.
  • [4] D. Kreutz, F. M. V. Ramos, and P. Verissimo, "Towards secure and dependable software-defined networks", in Proc. 2nd ACM SIG-COMM Worksh. Hot Topics in Softw. Def. Netw. HotSDN'13, Hong Kong, China, 2013, pp. 55-60.
  • [5] V. Tiwari, R. Parekh, and V. Patel, "A survey on vulnerabilities of open flow network and its impact on SDN/Open flow controller", World Academic J. Eng. Sci., vol. 1, no. 01:1005, 2014.
  • [6] S. Akbar Mehdi, J. Khalid, S. A. Khayam, "Revisiting traffc anomaly detection using software defined networking", in Recent Advances in Intrusion Detection, R. Sommer, D. Balzarotti, and G. Maier, Eds. LNCS, vol. 6961, pp. 161-180. Berlin Heidelberg, Springer, 2011.
  • [7] S. Dotcenko, A. Vladyko, and I. Letenko, "A fuzzy logic-based information security management for software-defined networks", in Proc. 16th Int. Cof. Adv. Commun. Technol. ICACT 2014, Pyeongchang, South Korea, 2014, pp. 167-171.
  • [8] K. Giotis, C. Argyropoulos, G. Androulidakis, D. Kalogeras, and V. Maglaris, "Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments", J. Comp. Netw., vol. 62, pp. 122-136, 2014.
  • [9] R. Braga, E. Mota, and A. Passito, "Lightweight DDoS flooding attack detection using NOX/OpenFlow", in Proc. 35th Ann. IEEE Conf. Local Comp. Netw. LCN 2010, Denver, Colorado, USA, 2010, pp. 408-415.
  • [10] OpenDaylight Platform [Online]. Available: https://www.opendaylight.org/
  • [11] Mininet - An Instant Virtual Network on your Laptop (or other PC) [Online]. Available: http://minimet.org
  • [12] A. S. Subaira and P. Anitha, "Efficient classification mechanism for network intrusion detection system based on data mining techniques: a survey", in 8th IEEE Int. Conf. Intell. Syhst. & Control ISCO 2014, Coimbatore, India, 2014, pp. 274-280.
  • [13] K. Choksi, B. Shah, and O. Kale, "Intrusion detection system using self organizing map: a surevey", Int. J. Engin. Res. Appl., vol. 4, no. 12, pp. 11-16, 2014.
  • [14] S. Osowski, Sieci neuronowe do przetwarzania informacji (Neural Networks for Information Processing). Warsaw: Publishing House of Warsaw University of Technology, 2013 (in Polish).
  • [15] "SOMz: Self Organizing Maps and random atlas" [Online]. Available: http://lcdm.astro.illinois.edu/static/code/mlz/MLZ-1.2/doc/html/somz.html
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-70b97c0f-8802-4149-9a24-e879d7d5bd8f
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