Denial-of-Sleep Attack Detection in NB-IoT Using Deep Learning
Main Article Content
Keywords
Deep learning, denial-of-sleep attack (DoSl), Internet-of-Things (IoT), NB-IoT, recurrent neural network (RNN)
Abstract
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.
Downloads
References
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. https://doi.org/10.1007/978-1-4842-4470-8
Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., & Gelenbe, E. (2018). IoT attack detection with deep learning. In ISCIS Security Workshop. Available at https://hal.laas.fr/hal-02062091
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. https://doi.org/10.1109/ACCESS.2017.2751586
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). https://colab.research.google.com/notebooks/intro.ipynb (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. https://doi.org/10.1155/2017/9863032
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. https://doi.org/10.1016/j.iot.2019.100059
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. https://doi.org/10.17706/IJCCE.2015.4.3.156-165
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). https://doi.org/10.5120/18040-8920
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. http://doi.org/10.1109/PlatCon.2016.7456805
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. https://doi.org/10.4304/jetwi.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. https://doi.org/10.1109/JIOT.2017.2739181
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. https://doi.org/10.1109/ACCESS.2018.2812835
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. https://doi.org/10.1109/ACCESS.2018.2881533
Saeedi, K. (2019). Machine learning for DDOS detection in packet core network for IoT. Masters Thesis, Luleå University of Technology. Available at https://www.diva-portal.org/smash/get/diva2:1360486/FULLTEXT02.pdf
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. https://doi.org/10.5220/0006639801080116
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. https://doi.org/10.18080/jtde.v8n3.299
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 https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2719
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. https://doi.org/10.1109/MCOM.2017.1600510CM
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. https://doi.org/10.1109/JIOT.2018.2825098
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. https://doi.org/10.1109/ACCESS.2017.2762418
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. https://doi.org/10.1109/TNNLS.2018.2886017
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.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Telecommunications Association Inc.
Tahani Bani-Yaseen, Princess Sumaya University for Technology
Tahani Bani-Yaseen received the B.Sc. degree in telecommunication engineering from Al- Balqa Applied University, Amman, Jordan, in 2015 and the M.Sc. degree in electrical engineering from Princess Sumaya University for Technology, Amman, Jordan, in 2020. She worked at Zain mobile telecom company as a network engineer, Amman, Jordan from 2015-2016. Her research interests include mobile communications, wireless communications and machine learning. She is a member of Jordan Engineers Association.
Ashraf Tahat, Princess Sumaya University for Technology
Ashraf Tahat is a Professor in the Department of Communications Engineering at Princess Sumaya University for Technology (PSUT) and the Vice-Chairman of IEEE Jordan Section. Dr. Tahat earned his B.Sc. and M.Sc. degrees in Electrical Engineering from the Illinois Institute of Technology (IllinoisTech), Chicago, USA, where he also received a Ph.D. in 2002, with a focus on communications and signal processing. Professor Tahat joined PSUT in 2005, where he participated in establishing the department of Communications Engineering and served as the Head of the Department from 2010 to 2012. Dr Tahat was also a Visiting Professor with McGill University in Montreal, Canada, in the Department of ECE, conducting research on modern communications systems (2012-2013). From 2002 to 2003, he was an Adjunct Professor at IllinoisTech, Chicago, USA. Before completing his doctoral studies, he was at Bell Labs-Lucent Technologies Inc (Nokia Networks), the Concept Center, working on first generation ADSL modem prototypes. He was also a senior staff engineer at 3Com Corporation in Illinois, USA, at the Personal Communications Division (1998-2000). Professor. Tahat is a Senior Member of IEEE, Eta Kappa Nu, and Tau Beta Pi honor societies.
Kira Kastell, Hamm-Lippstadt University of Applied Sciences
Kira Kastell is the President of Hamm-Lippstadt University of Applied Sciences since May 2021. Professor Kastell received the diploma (B.Sc. degree) in electrical engineering from the Frankfurt University of Applied Sciences, Frankfurt, Germany, and the diploma (M.Sc. degree) from FernUniversität in Hagen, Germany, and the Ph.D. degree from Technical University of Darmstadt, Darmstadt, Germany in 2007 with a focus on mobile communications and protocols for hybrid networks. She was a Professor with the Department of Computer Science and Engineering, Frankfurt University of Applied Sciences (FRA-UAS). She joined FRA-UAS in 2009. She served as Vice President for academic affairs 2013 to 2019. She was also teaching at Vietnamese-German University, Binh Doung, Vietnam for several years as member of the flying faculty. Since November 2019 she is chairwoman of the Association of Electrical and Information Engineering Departments in Germany. Since January 2021, Professor Kastell has been a member of the board of directors of ASIIN, the accreditation agency for courses in engineering, computer science, natural sciences and mathematics.
Talal A. Edwan, Al-Ahliyya Amman University
Talal A. Edwan's main work has been in computer system performance evaluation and engineering, network protocols and network algorithms. He received a BSc in Electronics Engineering with a First Class Honours from Princess Sumaya University for Technology (PSUT) in 2002, MSc in Networks Engineering with Distinction from University of Plymouth (UK) in 2005, and a PhD in Computer Networks from Loughborough University (UK) in 2010. Dr Talal served as an Assistant Professor in the Department of Computer Engineering at PSUT 2014-2019. He is an Assistant Professor in the Department of Computer Engineering at Al-Ahliyya Amman University (AAU) since 2020. His research interests are: Computer Networks, Network Congestion Control, Performance Evaluation/Engineering of Computer Systems/Networks and Queueing Theory.
