Abstract
Recent years have seen a surge in the usage of deep learning as a critical and adaptable tool for the Intrusion Detection Systems (IDSs) and in the Internet of Things (IoT). Detecting intrusions using deep learning is compared to other techniques, such as machine learning, which have been used in the past, in this research. There is a lot of expertise, advice, and ongoing maintenance required for most solutions, including machine learning. The fundamental advantage of deep learning over other techniques is that it eliminates the majority of the feature extraction process while maintaining high accuracy, efficiency, and system reliability. In this paper, an intrusion detection model is proposed based on deep learning technique. A Multi-Layer Perceptron (MLP) Neural Network model is implemented. The KDDCUP 99’ dataset is used in this research to test two deep learning architectures against each other and with previous works. Four hidden layers of ReLu activation, a softmax activation output, Adam optimizer, and early stopping validation loss monitoring are used in both topologies. Because they are implemented as multi-classification neural networks, they also use categorical cross entropy for loss function calculation. Form (10, 50, 10, 1) were the hidden layers in Model 1, while form (20, 20, 20, 1) were the hidden layers in Model 2. Model 1 architecture achieves a maximum accuracy of 99.88%, while Model 2 architecture achieves a maximum accuracy of 99.785%. The system’s efficiency and accuracy were tested with regard to the size of the samples as a factor.
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
Aldweesh, A., Derhab, A., Emam, A.Z.: Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl.-Based Syst. 189, 105124 (2020)
Allen, D.M.: The relationship between variable selection and data augmentation and a method for prediction. Technometrics 16, 125–127 (1974)
Anthony, O., Odeyabinya, J., Emmanuel, S.: Intrusion detection in Internet of Things (IoT). Int. J. Adv. Res. Comput. Sci. 9(1), 504–509 (2018)
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. ISBN 0-306-40671-3 (1981)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Wu, M., Jermaine, C.: Outlier detection by sampling with accuracy guarantees. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 767–772, August 2006
Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Clust. Comput. 22(4), 9889–9904 (2018). https://doi.org/10.1007/s10586-018-1847-2
El-Ghamrawy, S.M., Eldesouky, A.I.: An agent decision support module based on granular rough model. Int. J. Inf. Technol. Decis. Mak. 11(04), 793–820 (2012)
Chandrashekhar, A.M., Raghuveer, K.: Performance evaluation of data clustering techniques using KDD Cup-99 Intrusion detection data set. Int. J. Inf. Netw. Secur. 1(4), 294 (2012)
El-Ghamrawy, S.M., El-Desouky, A.I., Sherief, M.: Dynamic ontology mapping for communication in distributed multi-agent intelligent system. In: 2009 International Conference on Networking and Media Convergence, pp. 103–108. IEEE, March 2009
Dhanalakshmi, K.S., Kannapiran, B.: Analysis of KDD CUP dataset using multi-agent methodology with effective fuzzy based intrusion detection system. J. Appl. Secur. Res. 12(3), 424–439 (2017)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6. IEEE, July 2009
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009)
Goodfellow, Bengio, Courville: This table-filling strategy is sometimes called dynamic programming, p. 214 (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Lee, J., Park, K.: AE-CGAN model based high performance network intrusion detection system. Appl. Sci. 9(20), 4221 (2019)
Salama, M.A., Eid, H.F., Ramadan, R.A., Darwish, A., Hassanien, A.E.: Hybrid intelligent intrusion detection scheme. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds.) Soft Computing in Industrial Applications, pp. 293–303. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20505-7_26
Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 178–183. IEEE, February 2018
Soumyalatha, S.G.H.: Study of IoT: understanding IoT architecture, applications, issues and challenges. In: 1st International Conference on Innovations in Computing & Net-working (ICICN16), CSE, RRCE, May 2016. International Journal of Advanced Networking & Applications
Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019)
Karatas, G., Demir, O., Sahingoz, O.K.: Deep learning in intrusion detection systems. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 113–116. IEEE, December 2018
Lin, P., Ye, K., Xu, C.-Z.: Dynamic network anomaly detection system by using deep learning techniques. In: Da Silva, D., Wang, Q., Zhang, L.-J. (eds.) CLOUD 2019. LNCS, vol. 11513, pp. 161–176. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23502-4_12
Sethi, P., Sarangi, S.R.: Internet of Things: architectures, protocols, and applications. J. Electr. Comput. Eng. 2017, 1–25 (2017)
Xiao, L., Wan, X., Xiaozhen, L., Zhang, Y., Di, W.: IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Signal Process. Mag. 35(5), 41–49 (2018)
Engy, E.L., Ali, E.L., Sally, E.G.: An optimized artificial neural network approach based on sperm whale optimization algorithm for predicting fertility quality. Stud. Inform. Control 27(3), 349–358 (2018)
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 Switzerland AG
About this paper
Cite this paper
Elghamrawy, S.M., Lotfy, M.O., Elawady, Y.H. (2022). An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) Network. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-03918-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-03917-1
Online ISBN: 978-3-031-03918-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)