Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0975-5_31
Reference10 articles.
1. Zhang L, Yan H, Zhu Q (2020) An improved LSTM network intrusion detection method. In: 2020 IEEE 6th international conference on computer and communications (ICCC). Chengdu, China, pp 1765–1769. https://doi.org/10.1109/ICCC51575.2020.9344911
2. Choudhury S, Bhowal A (2015) Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. In: 2015 International conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM). Avadi, India, pp 89–95. https://doi.org/10.1109/ICSTM.2015.7225395
3. Kumar KP, Cherukuri RC (2019) Secure provenance-based communication using visual encryption. Int J Innov Comput Appl 10(3–4):194–206
4. Ravipati RD, Abualkibash M (2019) Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets-a review paper. Int J Comput Sci Inf Technol (IJCSIT) 11
5. Bhattacharjee PS, Fujail AKM, Begum SA (2017) A comparison of intrusion detection by K-means and fuzzy C-means clustering algorithm over the NSL-KDD dataset. In: 2017 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE
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