Approach on Machine Learning Techniques for Anomaly-Based Web Intrusion Detection Systems: Using CICIDS2017 Dataset-Reference-Cited by-同舟云学术

Approach on Machine Learning Techniques for Anomaly-Based Web Intrusion Detection Systems: Using CICIDS2017 Dataset

Published:2024 Issue: Volume: Page:59-72
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:en
Short-container-title:

Author:

Phulre Ajay Kumar,Verma Manoj,Mathur Jitendra Pratap Singh,Jain Sanat

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8135-9_6

Reference22 articles.

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2. Han LI (2011) Using a dynamic K-means algorithm to detect anomaly activities. In: 2011 seventh international conference on computational intelligence and security, pp 1049–1052

3. Sharafaldin I (2018) Intrusion detection evaluation dataset (CICIDS2017). Canadian Institute for Cybersecurity, Jan 2018. Accessed on 12 May 2020 [online]. Available https://www.unb.ca/cic/datasets/ids-2017.html

4. Lashkari AH, Draper-Gil G, Mamun MSI, Ghorbani AA (2017) Characterization of tor traffic using time based features. In: The proceedings of the 3rd international conference on information system security and privacy, SCITEPRESS, Porto, Portugal

5. Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA (2016) Characterization of encrypted and VPN traffic using time-related features. In: The proceedings of the 2nd international conference on information systems security and privacy (ICISSP 2016), Italy, pp 407–414

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