Deep learning based network intrusion detection system: a systematic literature review and future scopes
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10207-024-00896-y.pdf
Reference126 articles.
1. Abbasi, J.S., Bashir, F., Qureshi, K.N., Islam, M.N., Jeon, G.: Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine. Comput. Electr. Eng. 92, 1–12 (2021). https://doi.org/10.1016/j.compeleceng.2021.107094
2. Abdulganiyu, O.H., Tchakoucht, T.A., Saheed, Y.K.: A systematic literature review for network intrusion detection system (IDS). Int. J. Inf. Secur. 1–38 (2023). https://doi.org/10.1007/s10207-023-00682-2
3. Ahmad, Z., Khan, A.S., Shiang, C.W., Abdullah, J., Ahmad, F.: Network Intrusion Detection System: A systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommunications Technol. 1–29 (2020). https://doi.org/10.1002/ett.4150
4. Aldarwbi, M.Y., Lashkari, A.H., Ghorbani, A.A.: The sound of intrusion: A novel intrusion detection system. Comput. Electr. Eng. 104, 1–12 (2022). https://doi.org/10.1016/j.compeleceng.2022.108455
5. Al-Hawawreh, M., Sitnikova, E., Aboutorabb, N.: X-IIoTID: A connectivity-agnostic and device-agnostic Intrusion Data Set for Industrial Internet of things. IEEE Internet Things J., 3962–3977. (2022)
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