Abstract
Software-defined network (SDN) is an emerging technology that is being used widely to reduce the complexity of programming network functions. However, by splitting the control and data layers, the SDN architecture also attracts different types of attacks such as Distributed Denial of Service (DDoS). In recent years, several research studies addressed the security problem by introducing open datasets and classification techniques to detect attacks on SDN. The state-of-the-art techniques perform very well in a single dataset, i.e. when the training and testing datasets are from the same source. However, their performance reduces significantly in the presence of concept drift, i.e. if the testing dataset is collected from a different source than the training dataset. In this paper, we address this cross-dataset predictive issue by several concept drift detection techniques. The experimental results show that our techniques can improve performance in the cross-dataset scenario.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, A.A., Boukari, S., Bello, A.M., Muhammad, M.A.: A survey of intrusion detection techniques on software defined networking (SDN). In: International Journal of Innovative Science and Research Technology (2021)
Alhowaide, A., Alsmadi, I., Tang, J.: Ensemble detection model for IoT IDS. Internet Things 16, 100435 (2021)
Dang, Q.-V.: Studying machine learning techniques for intrusion detection systems. In: Dang, T.K., Küng, J., Takizawa, M., Bui, S.H. (eds.) FDSE 2019. LNCS, vol. 11814, pp. 411–426. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35653-8_28
Dang, Q.V.: Active learning for intrusion detection systems. In: IEEE, RIVF (2020)
Dang, Q.-V.: Understanding the decision of machine learning based intrusion detection systems. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds.) FDSE 2020. LNCS, vol. 12466, pp. 379–396. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63924-2_22
Dang, Q.V.: Improving the performance of the intrusion detection systems by the machine learning explainability. In: IJWIS (2021)
Dang, Q.-V.: Intrusion detection in software-defined networks. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds.) FDSE 2021. LNCS, vol. 13076, pp. 356–371. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91387-8_23
Dang, Q.V.: Machine learning for intrusion detection systems: recent developments and future challenges. In: Real-Time Applications of Machine Learning in Cyber-Physical Systems, pp. 93–118 (2022)
Dang, Q.V., François, J.: Utilizing attack enumerations to study sdn/nfv vulnerabilities. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 356–361. IEEE (2018)
Dang, Q.V., Ignat, C.L.: Computational trust model for repeated trust games. In: 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 34–41. IEEE (2016)
Elsayed, M.S., Le-Khac, N.A., Jurcut, A.D.: InSDN: a novel SDN intrusion dataset. IEEE Access 8, 165263–165284 (2020)
Ferguson-Walter, K., Fugate, S., Mauger, J., Major, M.: Game theory for adaptive defensive cyber deception. In: Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security, pp. 1–8 (2019)
Herrera-Semenets, V., Bustio-MartĂnez, L., Hernández-LeĂłn, R., van den Berg, J.: A multi-measure feature selection algorithm for efficacious intrusion detection. Knowl.-Based Syst. 227, 107264 (2021)
Ignaczak, L., Goldschmidt, G., Costa, C.A.D., Righi, R.D.R.: Text mining in cybersecurity: a systematic literature review. ACM Comput. Surv. (CSUR) 54(7), 1–36 (2021)
Ignat, C., Dang, Q., Shalin, V.L.: The influence of trust score on cooperative behavior. ACM Trans. Internet Technol. 19(4), 1–22 (2019)
Martin, R.A., Barnum, S.: Common weakness enumeration (cwe) status update. ACM SIGAda Ada Lett. 28(1), 88–91 (2008)
Mittal, S.: Performance evaluation of openflow SDN controllers. In: Abraham, A., Muhuri, P.K., Muda, A.K., Gandhi, N. (eds.) ISDA 2017. AISC, vol. 736, pp. 913–923. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76348-4_87
Nielsen, T.L., Abildskov, J., Harper, P.M., Papaeconomou, I., Gani, R.: The CAPEC database. J. Chem. Eng. Data 46(5), 1041–1044 (2001)
Pawlick, J., Zhu, Q.: Game Theory for Cyber Deception. SDGTFA, Springer, Cham (2021). https://doi.org/10.1007/978-3-030-66065-9
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. In: Advances in Neural Information Processing Systems, pp. 6638–6648 (2018)
Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp. 108–116 (2018)
Stallings, W.: Foundations of modern networking: SDN, NFV. IoT, and Cloud. Addison-Wesley Professional, QoE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dang, QV. (2022). Detecting Intrusion Using Multiple Datasets in Software-Defined Networks. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_55
Download citation
DOI: https://doi.org/10.1007/978-981-19-8069-5_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8068-8
Online ISBN: 978-981-19-8069-5
eBook Packages: Computer ScienceComputer Science (R0)