Unfolding the network dataset to understand the contribution of features for detecting malicious activities using AI/ML

Author:

Khatkar Monika,Kumar Kaushal,Kumar Brijesh

Publisher

Elsevier BV

Subject

General Medicine

Reference18 articles.

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2. S.J. Stolfo, W. Lee, A. Prodromidis, P.K. Chan, W. Fan, Cost-Based modeling and evaluation for data mining with application to fraud and intrusion detection: Results from the JAM project Cost-based Modeling and Evaluation for Data Mining with Application to Fraud and Intrusion Detection: Results from the JAM Project *, 1999, http://www.cs.columbia.edu/.

3. H.M. Harb, A.S. Desuky, Selecting Optimal Subset of Features for Intrusion Detection Systems Agent Based Services for the Semantic Web View Project Breast Masses Mammography View project, 2011. http://www.ripublication.com/acst.htm.

4. H. Benaddi, K. Ibrahimi, A. Benslimane, Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN, in: Proceedings - 2018 International Conference on Wireless Networks and Mobile Communications, WINCOM 2018, 2019, January 29. https://doi.org/10.1109/WINCOM.2018.8629718.

5. A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset;Parsaei;Int. J. Adv. Comput. Sci. Appl.,2016

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