Analysis of the Impact of Denial of Service Attacks on Centralized Control in Smart Cities

  • Evariste LogotaEmail author
  • Georgios Mantas
  • Jonathan Rodriguez
  • Hugo Marques
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 146)


The increasing threat of Denial of Service (DoS) attacks targeting Smart City systems impose unprecedented challenges in terms of service availability, especially against centralized control platforms due to their single point of failure issue. The European ARTEMIS co-funded project ACCUS (Adaptive Cooperative Control in Urban (sub) Systems) is focused on a centralized Integration and Coordination Platform (ICP) for urban subsystems to enable real-time collaborative applications across them and optimize their combined performance in Smart Cities. Hence, any outage of the ACCUS ICP, due to DoS attacks, can severely affect not only the interconnected subsystems but also the citizens. Consequently, it is of utmost importance for ACCUS ICP to be protected with the appropriate defense mechanisms against these attacks. Towards this direction, the measurement of the performance degradation of the attacked ICP server can be used for the selection of the most appropriate defense mechanisms. However, the suitable metrics are required to be defined. Therefore, this paper models and analyzes the impact of DoS attacks on the queue management temporal performance of the ACCUS ICP server in terms of system delay by using queueing theory.


Smart city security Denial of service attacks Security modeling Queueing theory 



The research leading to these results has received funding from the ARTEMIS Joint Undertaking Project ACCUS (ACCUS–ARTEMIS-005-2012/GA number 333020).


  1. 1.
    McGregory, S.: Preparing for the next DDoS attack. Netw. Secur. 5, 5–6 (2013)CrossRefGoogle Scholar
  2. 2.
    Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun. Surv. Tutorials. 15(4), 2046–2069 (2013)CrossRefGoogle Scholar
  3. 3.
    ACCUS (Adaptive Cooperative Control in Urban (sub) Systems).
  4. 4.
    Freiling, F.C., Holz, T., Wicherski, G.: Botnet tracking: exploring a root-cause methodology to prevent distributed denial-of-service attacks. In: di de Capitani di Vimercati, S., Syverson, P.F., Gollmann, D. (eds.) ESORICS 2005. LNCS, vol. 3679, pp. 319–335. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Specht, S.M., Lee, R.B.: Distributed denial of service: taxonomies of attacks, tools and countermeasures. In: 17th International Conference on Parallel and Distributed Computing Systems, San Francisco, California, USA, pp. 543–550 (2004)Google Scholar
  6. 6.
    Zukerman, M.: Introduction to Queueing Theory and Stochastic Teletraffic Models (2014).
  7. 7.

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Evariste Logota
    • 1
    Email author
  • Georgios Mantas
    • 1
  • Jonathan Rodriguez
    • 1
  • Hugo Marques
    • 1
  1. 1.Instituto de TelecomunicaçõesAveiroPortugal

Personalised recommendations