Abstract
Denial of Service attacks, which have become commonplace on the Information and Communications Technologies domain, constitute a class of threats whose main objective is to degrade or disable a service or functionality on a target. The increasing reliance of Cyber-Physical Systems upon these technologies, together with their progressive interconnection with other infrastructure and/or organizational domains, has contributed to increase their exposure to these attacks, with potentially catastrophic consequences. Despite the potential impact of such attacks, the lack of generality regarding the related works in the attack prevention and detection fields has prevented its application in real-world scenarios. This paper aims at reducing that effect by analyzing the behavior of classification algorithms with different dataset characteristics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Humayed, A., Lin, J., Li, F., Luo, B.: Cyber-physical systems security: a survey. IEEE Internet Things J. 4(6), 1802–1831 (2017). https://doi.org/10.1109/JIOT.2017.2703172
Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDOS) flooding attacks. IEEE Commun. Surv. Tutor. 15(4), 2046–2069 (2013). https://doi.org/10.1109/SURV.2013.031413.00127
Sabhnani, M., Serpen, G., More, K.K.: Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context. In: Proceedings of International Conference on Machine Learning: Models, Technologies, and Applications (MLMTA), January 2003, pp. 209–215 (2003). http://dl.acm.org/citation.cfm?id=1293805.1293811
Osanaiye, O., Cai, H., Choo, K.K.R., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. Eurasip J. Wirel. Commun. Netw. 2016(1), 130 (2016). https://doi.org/10.1186/s13638-016-0623-3
Su, M.Y.: Real-time anomaly detection systems for Denial-of-Service attacks by weighted k-nearest-neighbor classifiers. Expert Syst. Appl. 38(4), 3492–3498 (2011). https://doi.org/10.1016/j.eswa.2010.08.137
Goldenberg, N., Wool, A.: Accurate modeling of Modbus/TCP for intrusion detection in SCADA systems. Int. J. Crit. Infrastruct. Prot. 6(2), 63–75 (2013). https://doi.org/10.1016/j.ijcip.2013.05.001
Yoon, M., Ciocarlie, G.F.: Communication pattern monitoring : improving the utility of anomaly detection for industrial control systems. SENT 14(February), 110 (2014). https://doi.org/10.14722/sent.2014.23012
Erez, N., Wool, A.: Control variable classification, modeling and anomaly detection in Modbus/TCP SCADA systems. Int. J. Crit. Infrastruct. Prot. 10, 59–70 (2015). https://doi.org/10.1016/j.ijcip.2015.05.001
Markovic-Petrovic, J.D., Stojanovic, M.D.: Analysis of SCADA system vulnerabilities to DDoS attacks. In: 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services, TELSIKS 2013, vol. 2, pp. 591–594 (2013). https://doi.org/10.1109/TELSKS.2013.6704448
Kalluri, R., Mahendra, L., Kumar, R.K.S., Prasad, G.L.G.: Simulation and impact analysis of denial-of-service attacks on power SCADA. In: National Power Systems Conference, NPSC 2016, vol. 1 (2017). https://doi.org/10.1109/NPSC.2016.7858908
Hink, R.C.B., Beaver, J.M., Buckner, M.A., Morris, T., Adhikari, U., Pan, S.: Machine learning for power system disturbance and cyber-attack discrimination. In: 7th International Symposium on Resilient Control Systems, ISRCS 2014 (2014). https://doi.org/10.1109/ISRCS.2014.6900095
Acknowledgements
This work was supported by the ATENA European H2020 Project (H2020-DS-2015-1 Project 700581).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Frazão, I., Abreu, P.H., Cruz, T., Araújo, H., Simões, P. (2019). Denial of Service Attacks: Detecting the Frailties of Machine Learning Algorithms in the Classification Process. In: Luiijf, E., Žutautaitė, I., Hämmerli, B. (eds) Critical Information Infrastructures Security. CRITIS 2018. Lecture Notes in Computer Science(), vol 11260. Springer, Cham. https://doi.org/10.1007/978-3-030-05849-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-05849-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05848-7
Online ISBN: 978-3-030-05849-4
eBook Packages: Computer ScienceComputer Science (R0)