A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset
Main Article Content
Abstract
The growth of Internet of Things (IoT) gadgets has ushered in a new era of connectedness and convenience, but it has also sparked worries about security flaws. Long Short-Term Memory (LSTM) networks are used in this research's use of intrusion detection as a novel strategy to strengthen IoT security. The proposed LSTM-based model excels in detecting both known and evolving cyber-attack patterns with an accuracy rate of 98.75% and an F1 score of 98.59% in extensive experimental evaluations using the vast CIC-IoT2023 dataset, representing a varied array of IoT network traffic scenarios. This research contributes significantly to IoT security while addressing the urgent need for adaptable intrusion detection systems to defend against changing cyber threats. It is an essential step toward ensuring IoT technology's long-term development and dependability in a world that is becoming more interconnected.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Accepted 2023-12-24
Published 2024-05-21
References
Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I. and Guizani, M., 2020. A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security. IEEE Communications Surveys & Tutorials, 22(3), pp.1646–1685. Available from: https://doi.org/10.1109/COMST.2020.2988293. DOI: https://doi.org/10.1109/COMST.2020.2988293
Chesney, S., Roy, K. and Khorsandroo, S., 2021. Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks. In: K. Arai, S. Kapoor and R. Bhatia, eds. Intelligent Systems and Applications. Cham: Springer International Publishing, pp.679–686. Available from: https://doi.org/10.1007/978-3-030-55190-2_53. DOI: https://doi.org/10.1007/978-3-030-55190-2_53
De La Torre Parra, G., Rad, P., Choo, K.K.R. and Beebe, N., 2020. Detecting Internet of Things attacks using distributed deep learning. Journal of Network and Computer Applications, 163, p.102662. Available from: https://doi.org/https://doi.org/10.1016/j.jnca.2020.102662. DOI: https://doi.org/10.1016/j.jnca.2020.102662
Deogirikar, J. and Vidhate, A., 2017. Security attacks in IoT: A survey. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). pp.32–37. Available from: https://doi.org/10.1109/I-SMAC.2017.8058363. DOI: https://doi.org/10.1109/I-SMAC.2017.8058363
Dolphin, R., 2021. LSTM Networks | A Detailed Explanation. Medium. Available from: https://towardsdatascience.com/lstm-networks-a-detailed-explanation-8fae6aefc7f9.
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R. and Schmidhuber, J., 2017. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), pp.2222–2232. Available from: https://doi.org/10.1109/TNNLS.2016.2582924. DOI: https://doi.org/10.1109/TNNLS.2016.2582924
HaddadPajouh, H., Dehghantanha, A., Khayami, R. and Choo, K.K.R., 2018. A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting. Future Generation Computer Systems, 85, pp.88–96. Available from: https://doi.org/10.1016/j.future.2018.03.007. DOI: https://doi.org/10.1016/j.future.2018.03.007
Hochreiter, S. and Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8), pp.1735–1780. Available from: https://doi.org/10.1162/neco.1997.9.8.1735. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Ibitoye, O., Shafiq, O. and Matrawy, A., 2019. Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks. 2019 IEEE Global Communications Conference (GLOBECOM). pp.1–6. Available from: https://doi.org/10.1109/GLOBECOM38437.2019.9014337. DOI: https://doi.org/10.1109/GLOBECOM38437.2019.9014337
Illing, D., 2023. Common Cyber-Attacks in the IoT | GlobalSign. GlobalSign. Available from: https://www.globalsign.com/en/blog/common-cyber-attacks-in-the-iot.
Ingolfsson, T.M., 2021. Insights into LSTM architecture. Available from: https://thorirmar.com/post/insight_into_lstm/.
Long Short-Term Memory Networks (LSTM)- simply explained!, 2022. Available from: https://databasecamp.de/en/ml/lstms.
Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R. and Ghorbani, A.A., 2023. CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors, 23(13). Available from: https://doi.org/10.3390/s23135941. DOI: https://doi.org/10.3390/s23135941
Pajouh, H.H., Javidan, R., Khayami, R., Dehghantanha, A. and Choo, K.K.R., 2019. A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks. IEEE Transactions on Emerging Topics in Computing, 7(2), pp.314–323. Available from: https://doi.org/10.1109/TETC.2016.2633228. DOI: https://doi.org/10.1109/TETC.2016.2633228
Qin, Z., Kim, D. and Gedeon, T., 2020. Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator. 1911.10688, Available from: https://doi.org/10.48550/arXiv.1911.10688.
Ramachandran, P., Zoph, B. and Le, Q.V., 2017. Searching for Activation Functions. 1710.05941, Available from: https://doi.org/10.48550/arXiv.1710.05941.
Roy, B. and Cheung, H., 2018. A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network. 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). pp.1–6. Available from: https://doi.org/10.1109/ATNAC.2018.8615294. DOI: https://doi.org/10.1109/ATNAC.2018.8615294
Sahu, A.K., Sharma, S., Tanveer, M. and Raja, R., 2021. Internet of Things attack detection using hybrid Deep Learning Model. Computer Communications, 176, pp.146–154. Available from: https://doi.org/10.1016/j.comcom.2021.05.024. DOI: https://doi.org/10.1016/j.comcom.2021.05.024
Sengupta, J., Ruj, S. and Das Bit, S., 2020. A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, p.102481. Available from: https://doi.org/10.1016/j.jnca.2019.102481. DOI: https://doi.org/10.1016/j.jnca.2019.102481
Van, N.T., Thinh, T.N. and Sach, L.T., 2017. An anomaly-based network intrusion detection system using Deep learning. 2017 International Conference on System Science and Engineering (ICSSE). pp.210–214. Available from: https://doi.org/10.1109/ICSSE.2017.8030867. DOI: https://doi.org/10.1109/ICSSE.2017.8030867
What are Recurrent Neural Networks?, 2021. Available from: https://databasecamp.de/en/ml/recurrent-neural-network.