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Erschienen in: Wireless Personal Communications 4/2021

06.02.2021

Detection and Analysis of TCP-SYN DDoS Attack in Software-Defined Networking

verfasst von: Rochak Swami, Mayank Dave, Virender Ranga

Erschienen in: Wireless Personal Communications | Ausgabe 4/2021

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Abstract

Software-defined networking (SDN) is an advanced networking technology that yields flexibility with cost-efficiency as per the business requirements. SDN breaks the vertical integration of control and data plane and promotes centralized network management. SDN allows data intensive applications to work more efficiently by making the network dynamically configurable. With the growing development of SDN technology, the issue of security becomes critical because of its architectural characteristics. Currently, Distributed denial of service (DDoS) is one of the most powerful attacks that cause the services to be unavailable for normal users. DDoS seeks to consume the resources of the SDN controller with the intention to slow down working of the network. In this paper, a detailed analysis of the effect of spoofed and non-spoofed TCP-SYN flooding attacks on the controller resources in SDN is presented. We also suggest a machine learning based intrusion detection system. Five different classification models belong to a variety of families are used to classify the traffic, and evaluated using different performance indicators. Cross-validation technique is used to validate the classification models. This work enables better features to be extracted and classify the traffic efficiently. The experimental results reveal significantly good performance with all the considered classification models.

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Literatur
1.
Zurück zum Zitat Singh, J., & Behal, S. (2020). Detection and mitigation of ddos attacks in sdn: A comprehensive review, research challenges and future directions. Computer Science Review, 37(100), 279.MATH Singh, J., & Behal, S. (2020). Detection and mitigation of ddos attacks in sdn: A comprehensive review, research challenges and future directions. Computer Science Review, 37(100), 279.MATH
2.
Zurück zum Zitat Hakiri, A., Gokhale, A., Berthou, P., Schmidt, D. C., & Gayraud, T. (2014). Software-defined networking: Challenges and research opportunities for future internet. Computer Networks, 75, 453–471.CrossRef Hakiri, A., Gokhale, A., Berthou, P., Schmidt, D. C., & Gayraud, T. (2014). Software-defined networking: Challenges and research opportunities for future internet. Computer Networks, 75, 453–471.CrossRef
3.
Zurück zum Zitat Kirkpatrick, K. (2013). Software-defined networking. Communication ACM, 56, 16–19. Kirkpatrick, K. (2013). Software-defined networking. Communication ACM, 56, 16–19.
5.
Zurück zum Zitat McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., et al. (2008). Openflow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 38(2), 69–74.CrossRef McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., et al. (2008). Openflow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 38(2), 69–74.CrossRef
7.
Zurück zum Zitat Goransson, P., Black, C., & Culver, T. (2016). Software defined networks: A comprehensive approach. Morgan Kaufmann. Goransson, P., Black, C., & Culver, T. (2016). Software defined networks: A comprehensive approach. Morgan Kaufmann.
8.
Zurück zum Zitat Kreutz, D., Ramos, F. M., Verissimo, P., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015a). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103, 14–76.CrossRef Kreutz, D., Ramos, F. M., Verissimo, P., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015a). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103, 14–76.CrossRef
9.
Zurück zum Zitat Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51(2), 114–119.CrossRef Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51(2), 114–119.CrossRef
10.
Zurück zum Zitat Swami, R., Dave, M., & Ranga, V. (2019). Software-defined Networking-based DDoS Defense Mechanisms. ACM Computing Surveys (CSUR), 52(2), 28.CrossRef Swami, R., Dave, M., & Ranga, V. (2019). Software-defined Networking-based DDoS Defense Mechanisms. ACM Computing Surveys (CSUR), 52(2), 28.CrossRef
11.
Zurück zum Zitat Douligeris, C., & Mitrokotsa, A. (2004). DDoS attacks and defense mechanisms: Classification and state-of-the-art. Computer Networks, 44, 643–666.CrossRef Douligeris, C., & Mitrokotsa, A. (2004). DDoS attacks and defense mechanisms: Classification and state-of-the-art. Computer Networks, 44, 643–666.CrossRef
12.
Zurück zum Zitat Specht, S. M., & Lee, R. B. (2003). Distributed Denial of Service: Taxonomies of Attacks. Tools and Countermeasures, Princeton architecture laboratory for multimedia and security: Tech. rep., technical report. Specht, S. M., & Lee, R. B. (2003). Distributed Denial of Service: Taxonomies of Attacks. Tools and Countermeasures, Princeton architecture laboratory for multimedia and security: Tech. rep., technical report.
13.
Zurück zum Zitat Ramachandran, S., & Shanmugam, V. (2017). Impact of dos attack in software defined network for virtual network. Wireless Personal Communications, 94(4), 2189–2202.CrossRef Ramachandran, S., & Shanmugam, V. (2017). Impact of dos attack in software defined network for virtual network. Wireless Personal Communications, 94(4), 2189–2202.CrossRef
14.
Zurück zum Zitat Dayal, N., Maity, P., Srivastava, S., & Khondoker, R. (2016). Research trends in security and DDoS in SDN. Security and Communication Networks, 9(18), 6386–6411.CrossRef Dayal, N., Maity, P., Srivastava, S., & Khondoker, R. (2016). Research trends in security and DDoS in SDN. Security and Communication Networks, 9(18), 6386–6411.CrossRef
15.
Zurück zum Zitat Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2015). Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), 602–622.CrossRef Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2015). Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Communications Surveys & Tutorials, 18(1), 602–622.CrossRef
21.
Zurück zum Zitat Swami, R., Dave, M., & Ranga, V. (2020a). DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN. In Security and Privacy Issues in Sensor Networks and IoT, IGI Global (pp. 193–214). Swami, R., Dave, M., & Ranga, V. (2020a). DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN. In Security and Privacy Issues in Sensor Networks and IoT, IGI Global (pp. 193–214).
22.
Zurück zum Zitat Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other botnets. Computer, 50(7), 80–84.CrossRef Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other botnets. Computer, 50(7), 80–84.CrossRef
23.
Zurück zum Zitat Kambourakis, G., Kolias, C., & Stavrou, A. (2017). The mirai botnet and the iot zombie armies. In MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM) (pp. 267–272). IEEE. Kambourakis, G., Kolias, C., & Stavrou, A. (2017). The mirai botnet and the iot zombie armies. In MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM) (pp. 267–272). IEEE.
24.
Zurück zum Zitat Michie, D., Spiegelhalter, D. J., Taylor, C., et al. (1994). Machine learning. Neural and Statistical Classification 13. Michie, D., Spiegelhalter, D. J., Taylor, C., et al. (1994). Machine learning. Neural and Statistical Classification 13.
25.
Zurück zum Zitat Moustafa, N., Hu, J., & Slay, J. (2019). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128, 33–55.CrossRef Moustafa, N., Hu, J., & Slay, J. (2019). A holistic review of network anomaly detection systems: A comprehensive survey. Journal of Network and Computer Applications, 128, 33–55.CrossRef
29.
Zurück zum Zitat Swami, R., Dave, M., & Ranga, V. (2020b). Voting-based intrusion detection framework for securing software-defined networks. Concurrency and Computation: Practice and Experience p e5927. Swami, R., Dave, M., & Ranga, V. (2020b). Voting-based intrusion detection framework for securing software-defined networks. Concurrency and Computation: Practice and Experience p e5927.
30.
Zurück zum Zitat Mirkovic, J., & Reiher, P. (2004). A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communication Review, 34(2), 39–53.CrossRef Mirkovic, J., & Reiher, P. (2004). A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communication Review, 34(2), 39–53.CrossRef
33.
Zurück zum Zitat Yu, Y., Guo, L., Liu, Y., Zheng, J., & Zong, Y. (2018). An efficient SDN-based DDoS attack detection and rapid response platform in vehicular networks. IEEE Access, 6, 44570–44579.CrossRef Yu, Y., Guo, L., Liu, Y., Zheng, J., & Zong, Y. (2018). An efficient SDN-based DDoS attack detection and rapid response platform in vehicular networks. IEEE Access, 6, 44570–44579.CrossRef
34.
Zurück zum Zitat Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W., & Peng, J. (2018). XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud. In IEEE, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 251–256). Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W., & Peng, J. (2018). XGBoost Classifier for DDoS Attack Detection and Analysis in SDN-Based Cloud. In IEEE, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 251–256).
35.
Zurück zum Zitat Ye, J., Cheng, X., Zhu, J., Feng, L., & Song, L. (2018). A DDoS attack detection method based on SVM in software defined network. Security and Communication Networks, 2018, 1–8. Ye, J., Cheng, X., Zhu, J., Feng, L., & Song, L. (2018). A DDoS attack detection method based on SVM in software defined network. Security and Communication Networks, 2018, 1–8.
37.
Zurück zum Zitat Han, B., Yang, X., Sun, Z., Huang, J., & Su, J. (2018). OverWatch: A cross-plane DDoS attack defense framework with collaborative intelligence in SDN. Security and Communication Networks 2018. Han, B., Yang, X., Sun, Z., Huang, J., & Su, J. (2018). OverWatch: A cross-plane DDoS attack defense framework with collaborative intelligence in SDN. Security and Communication Networks 2018.
38.
Zurück zum Zitat Zhu, L., Karim, M. M., Sharif, K., Li, F., Du, X., & Guizani, M. (2019). Sdn controllers: Benchmarking & performance evaluation. arXiv preprint arXiv:190204491. Zhu, L., Karim, M. M., Sharif, K., Li, F., Du, X., & Guizani, M. (2019). Sdn controllers: Benchmarking & performance evaluation. arXiv preprint arXiv:190204491.
39.
Zurück zum Zitat Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE symposium on security and privacy (pp. 305–316). IEEE. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE symposium on security and privacy (pp. 305–316). IEEE.
40.
Zurück zum Zitat Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493–501.CrossRef Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493–501.CrossRef
42.
43.
Zurück zum Zitat Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674.MathSciNetCrossRef Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674.MathSciNetCrossRef
44.
Zurück zum Zitat Ying, C., Qi-Guang, M., Jia-Chen, L., & Lin, G. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745–758.CrossRef Ying, C., Qi-Guang, M., Jia-Chen, L., & Lin, G. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745–758.CrossRef
45.
Zurück zum Zitat Kleinbaum, D. G., Dietz, K., Gail, M., Klein, M., & Klein, M. (2002). Logistic regression. Berlin: Springer. Kleinbaum, D. G., Dietz, K., Gail, M., Klein, M., & Klein, M. (2002). Logistic regression. Berlin: Springer.
46.
Zurück zum Zitat Kubat, M. (1999). Neural networks: A comprehensive foundation by Simon Haykin, Macmillan. The Knowledge Engineering Review, 13, 409–412.CrossRef Kubat, M. (1999). Neural networks: A comprehensive foundation by Simon Haykin, Macmillan. The Knowledge Engineering Review, 13, 409–412.CrossRef
52.
Zurück zum Zitat Shalimov, A., Zuikov, D., Zimarina, D., Pashkov, V., & Smeliansky, R. (2013). Advanced study of SDN/OpenFlow controllers. In Proceedings of the 9th central & eastern european software engineering conference (p. 1). ACM. Shalimov, A., Zuikov, D., Zimarina, D., Pashkov, V., & Smeliansky, R. (2013). Advanced study of SDN/OpenFlow controllers. In Proceedings of the 9th central & eastern european software engineering conference (p. 1). ACM.
54.
Zurück zum Zitat Kohavi, R., et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, 14, 1137–1145. Kohavi, R., et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, 14, 1137–1145.
55.
Zurück zum Zitat Panda, M., Abraham, A., & Patra, M. R. (2012). A hybrid intelligent approach for network intrusion detection. Procedia Engineering, 30, 1–9.CrossRef Panda, M., Abraham, A., & Patra, M. R. (2012). A hybrid intelligent approach for network intrusion detection. Procedia Engineering, 30, 1–9.CrossRef
56.
Zurück zum Zitat Shenfield, A., Day, D., & Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express, 4(2), 95–99.CrossRef Shenfield, A., Day, D., & Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express, 4(2), 95–99.CrossRef
57.
Zurück zum Zitat Bhavsar, Y. B., & Waghmare, K. C. (2013). Intrusion detection system using data mining technique: Support vector machine. International Journal of Emerging Technology and Advanced Engineering, 3(3), 581–586. Bhavsar, Y. B., & Waghmare, K. C. (2013). Intrusion detection system using data mining technique: Support vector machine. International Journal of Emerging Technology and Advanced Engineering, 3(3), 581–586.
Metadaten
Titel
Detection and Analysis of TCP-SYN DDoS Attack in Software-Defined Networking
verfasst von
Rochak Swami
Mayank Dave
Virender Ranga
Publikationsdatum
06.02.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08127-6

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