Denial-of-Sleep Attack Detection in NB-IoT Using Deep Learning

Main Article Content

Tahani Bani-Yaseen
Ashraf Tahat
Kira Kastell
Talal A. Edwan


Deep learning, denial-of-sleep attack (DoSl), Internet-of-Things (IoT), NB-IoT, recurrent neural network (RNN)


With increasing Internet-of-Things (IoT) protocols and connectivity, a growing number of attacks are emerging in the associated networks. This work presents approaches using deep learning (DL) to detect attacks in an IoT environment, particularly in narrowband Internet-of-Things (NB-IoT). By virtue of its low cost, low complexity and limited energy, an NB-IoT device will not likely permit cutting-edge security mechanisms, leaving it vulnerable to, for example, denial-of-sleep (DoSl) attacks. For performance analysis, a NB-IoT network was simulated, using ns-3, to generate a novel dataset to represent an implementation of DoSl attacks. After preprocessing, the dataset was presented to a collection of machine learning (ML) models to evaluate their performance. The considered DL recurrent neural network (RNN) models have proven capable of reliably classifying traffic, with very high accuracy, into either a DoSl attack or a normal record. The performance of a long short-term memory (LSTM) classifier has provided accuracies up to 98.99%, with a detection time of 2.54 x 10-5 second/record, surpassing performance of a gated recurrent unit (GRU). RNN DL models have superior performance in terms of accuracy of detecting DoSl attacks in NB-IoT networks, when compared with other ML algorithms, including support vector machine, Gaussian naïve-Bayes, and logistic regression. 


Download data is not yet available.
Abstract 178 | 532-PDF-v10n3pp14-38 Downloads 7


Aggarwal, C. C. (2018). Neural Networks and Deep Learning. Springer.
Al-Rashdan, W. Y., & Tahat, A. (2020). A comparative performance evaluation of machine learning algorithms for fingerprinting based localization in DM-MIMO wireless systems relying on big data techniques. IEEE Access, 8, 109522–109534.
Bisong, E. (2019). Google Colaboratory, pp. 59–64 in: Bisong, E., Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress.
Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., & Gelenbe, E. (2018). IoT attack detection with deep learning. In ISCIS Security Workshop. Available at
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
Chaabane, M., Williams, R. M., Stephens, A. T., & Park, J. W. (2020). circDeep: deep learning approach for circular RNA classification from other long non-coding RNA. Bioinformatics, 36(1), 73–80.
Chen, M., Miao, Y., Hao, Y., & Hwang, K. (2017). Narrow Band Internet of Things. IEEE Access, 5, 20557–20577.
Ehsan, H., & Khan, F. A. (2012). Malicious AODV: implementation and analysis of routing attacks in MANETs. In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 1181–1187.
El Soussi, M., Zand, P., Pasveer, F., & Dolmans, G. (2018). Evaluating the performance of eMTC and NB-IoT for smart city applications. In 2018 IEEE International Conference on Communications (ICC), 1–7.
Fattah, H. (2018). 5G LTE Narrowband Internet of Things (NB-IoT). CRC Press.
Google Colaboratory (Colab). (2021). (Accessed 12 August 2021).
Gunasekaran, M., & Periakaruppan, S. (2017). GA-DoSLD: genetic algorithm based denial-of-sleep attack detection in WSN. Security and Communication Networks, 2017.
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059.
Hassoubah, R. S., Solaiman, S. M., & Abdullah, M. A. (2015). Intrusion detection of hello flood attack in WSNs using location verification scheme. International Journal of Computer and Communication Engineering, 4(3), 156.
John, G. H., & Langley, P. (2013). Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964.
Kaur, S., & Ataullah, M. (2014). Securing the wireless sensor network from denial of sleep attack by isolating the nodes. International Journal of Computer Applications, 103(1).
Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. 2016 International Conference on Platform Technology and Service (PlatCon), 1–5.
Le Cessie, S., & Van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 41(1), 191–201.
Li, Z., He, D., Tian, F., Chen, W., Qin, T., Wang, L., & Liu, T. (2018). Towards binary-valued gates for robust LSTM training. In International Conference on Machine Learning, 2995–3004.
Liberg, O., Sundberg, M., Wang, E., Bergman, J., & Sachs, J. (2017). Cellular Internet of things: technologies, standards, and performance. Academic Press.
Mahalakshmi, G., & Subathra, P. (2014). A survey on prevention approaches for denial of sleep attacks in wireless networks. Journal of Emerging Technologies in Web Intelligence, 6(1), 106–110.
Martiradonna, S., Grassi, A., Piro, G., Grieco, L. A., & Boggia, G. (2018). An open source platform for exploring NB-IoT system performance. In European Wireless 2018; 24th European Wireless Conference, 1–6.
Martiradonna, S., Piro, G., & Boggia, G. (2019). On the evaluation of the NB-IoT random access procedure in monitoring infrastructures. Sensors, 19(14), 3237.
Miao, Y., Li, W., Tian, D., Hossain, M. S., & Alhamid, M. F. (2017). Narrowband Internet of Things: Simulation and modeling. IEEE Internet of Things Journal, 5(4), 2304–2314.
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018). Convolutional neural networks based fire detection in surveillance videos. IEEE Access, 6, 18174–18183.
Niu, Y., Gao, D., Gao, S., & Chen, P. (2012). A robust localization in wireless sensor networks against wormhole attack. Journal of Networks, 7(1), 187.
Popli, S., Jha, R. K., & Jain, S. (2018). A survey on energy efficient Narrowband Internet of Things (NBIoT): architecture, application and challenges. IEEE Access, 7, 16739–16776.
Saeedi, K. (2019). Machine learning for DDOS detection in packet core network for IoT. Masters Thesis, Luleå University of Technology. Available at
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP 2018), 1, 108–116.
Tahat, A., Awad, R., Baydoun, N., Al-Nabih, S., & Edwan, T. A. (2021). An Empirical Evaluation of Machine Learning Algorithms for Indoor Localization using Dual-Band WiFi. In 2nd European Symposium on Software Engineering, 1–6.
Tahat, A., Ersan, B., Muhesen, L., Shakhshir, Z., & Edwan, T. A. (2020). A compact 38 GHz millimetre-wave MIMO antenna array for 5G mobile systems. Journal of Telecommunications and the Digital Economy, 8(3), 44–59.
Tang, T. A., McLernon, D., Mhamdi, L., Zaidi, S. A. R., & Ghogho, M. (2019). Intrusion detection in SDN-based networks: Deep recurrent neural network approach. In Deep Learning Applications for Cyber Security, 175–195. Springer.
TR-45.820. (2015). Cellular system support for ultra-low complexity and low throughput Internet of Things. V2.1.0. 3GPP. Available at
Wang, Y., Liao, W., & Chang, Y. (2018). Gated recurrent unit network-based short-term photo-voltaic forecasting. Energies, 11(8), 2163.
Wang, Y.-P. E., Lin, X., Adhikary, A., Grovlen, A., Sui, Y., Blankenship, Y., & Razaghi, H. S. (2017). A primer on 3GPP Narrowband Internet of Things. IEEE Communications Magazine, 55(3), 117–123.
Wei, F., & Nguyen, U. T. (2019). Twitter bot detection using bidirectional long short-term memory neural networks and word embeddings. In 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 101–109.
Xu, T., & Darwazeh, I. (2018). Non-orthogonal Narrowband Internet of Things: A design for saving bandwidth and doubling the number of connected devices. IEEE Internet of Things Journal, 5(3), 2120–2129.
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.
Yuan, X., He, P., Zhu, Q., & Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE transactions on neural networks and learning systems, 30(9), 2805–2824.
Zadeh, M. R., Amin, S., Khalili, D., & Singh, V. P. (2010). Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water resources management, 24(11), 2673–2688.

Most read articles by the same author(s)