A Review of Anomaly Based Multiple Intrusion Detection Methods Using a Feature Based Deep Learning Approach
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3810-6_20
Reference13 articles.
1. Abdulraheem MH, Ibraheem NB (2019) A detailed analysis of new intrusion detection dataset. JTAIT 97(17):4519–4537
2. Ahmad I et al (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE 6:33789–33795. https://doi.org/10.1109/ACCESS.2018.2841987
3. Aljawarneh S et al (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160. https://doi.org/10.1016/j.jocs.2017.03.006
4. Al-Turaiki I, Altwaijry N (2021) A convolutional neural network for improved anomaly-based network intrusion detection. Big Data 1(3):233–252. https://doi.org/10.1089/big.2020.0263
5. Das A et al (2022) Anomaly-based network intrusion detection using ensemble machine learning approach. IJACSA 13(2):635–645. https://doi.org/10.14569/IJACSA.2022.0130275
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