Abstract
An intrusion detection system (IDS) is an essential component of computer networks to detect and secure the system and environment from malicious activities and anomalous attacks. The convolutional neural network (CNN) is a popular deep learning algorithm that has been broadly applied in the field of computer vision. More recently, several researchers attempted to apply CNN for IDSs. However, the majority of these ignore the influence of the overfitting problem with the implementation of deep learning algorithms, which can impact the robustness of CNN-based anomaly detection systems. In this paper, we investigate the use of CNN for IDSs and propose a technique to enhance its performance by using two popular regularization techniques to address the overfitting problem. Our technique improves the capability of IDSs in detection of unseen intrusion events. We use InSDN benchmark dataset to train and evaluate the performance of our technique. The experimental results demonstrate that the regularization methods can improve the performance of CNN-based anomaly detection models for the software-defined networking (SDN) environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Çavuşoğlu, Ü.: A new hybrid approach for intrusion detection using machine learning methods. Appl. Intell. 49(7), 2735–2761 (2019). https://doi.org/10.1007/s10489-018-01408-x
Halimaa, A., Sundarakantham, K.: Machine learning based intrusion detection system. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 916–920. IEEE (2019)
Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M., Abuzneid, A.: Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics 8(3), 322 (2019)
Alkasassbeh, M., Almseidin, M.: Machine learning methods for network intrusion detection. arXiv preprint arXiv:1809.02610 (2018)
Taher, K.A., Jisan, B.M.Y., Rahman, M.M.: Network intrusion detection using supervised machine learning technique with feature selection. In: 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 643–646. IEEE (2019)
Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: InSDN: a novel SDN intrusion dataset. IEEE Access 8, 165 263–165 284 (2020)
Althubiti, S.A., Jones, E.M., Roy, K.: LSTM for anomaly-based network intrusion detection. In: 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1–3. IEEE (2018)
Elsayed, M.S., Le-Khac, N.-A., Dev, S., Jurcut, A.D.: DDoSNet: a deep-learning model for detecting network attacks. In: 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), pp. 391–396. IEEE (2020)
Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: Detecting abnormal traffic in large-scale networks. In: 2020 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–7. IEEE (2020)
Said Elsayed, M., Le-Khac, N.-A., Dev, S., Jurcut, A.D.: Network anomaly detection using LSTM based autoencoder. In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 37–45 (2020)
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018)
Al-Qatf, M., Lasheng, Y., Al-Habib, M., Al-Sabahi, K.: Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access 6, 52 843–52 856 (2018)
Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: Dealing with covid-19 network traffic spikes [cybercrime and forensics]. IEEE Secur. Priv. 19(1), 90–94 (2021)
Jahromi, H.Z., Hines, A., Delaney, D.T.: Towards application-aware networking: ML-based end-to-end application KPI/QoE metrics characterization in SDN. In: 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 126–131. IEEE (2018)
Jahromi, H.Z., Delaney, D.T.: An application awareness framework based on SDN and machine learning: defining the roadmap and challenges. In: 2018 10th International Conference on Communication Software and Networks (ICCSN), pp. 411–416. IEEE (2018)
Scott-Hayward, S., O’Callaghan, G., Sezer, S.: SDN security: a survey. In: 2013 IEEE SDN for Future Networks and Services (SDN4FNS), pp. 1–7. IEEE (2013)
Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 689–692 (2015)
Zhou, D., Yan, Z., Fu, Y., Yao, Z.: A survey on network data collection. J. Netw. Comput. Appl. 116, 9–23 (2018)
Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Khan, R.U., Zhang, X., Alazab, M., Kumar, R.: An improved convolutional neural network model for intrusion detection in networks. In: 2019 Cybersecurity and Cyberforensics Conference (CCC), pp. 74–77. IEEE (2019)
Yong, L., Bo, Z.: An intrusion detection model based on multi-scale CNN. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 214–218. IEEE (2019)
Hu, Z., Wang, L., Qi, L., Li, Y., Yang, W.: A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network. IEEE Access 8, 195 741–195 751 (2020)
Xiao, Y., Xing, C., Zhang, T., Zhao, Z.: An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access 7, 42 210–42 219 (2019)
Jiang, K., Wang, W., Wang, A., Wu, H.: Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access 8, 32 464–32 476 (2020)
Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020). https://doi.org/10.1007/s10462-020-09825-6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Elsayed, M.S., Jahromi, H.Z., Nazir, M.M., Jurcut, A.D. (2021). The Role of CNN for Intrusion Detection Systems: An Improved CNN Learning Approach for SDNs. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-78459-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-78459-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78458-4
Online ISBN: 978-3-030-78459-1
eBook Packages: Computer ScienceComputer Science (R0)