A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection-Reference-Cited by-同舟云学术

A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection

Published:2023-08-29 Issue: Volume: Page:
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Container-title:Proceedings of the 18th International Conference on Availability, Reliability and Security
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Author:

Catillo Marta¹^ORCID,Del Vecchio Andrea¹^ORCID,Pecchia Antonio¹^ORCID,Villano Umberto¹^ORCID

Affiliation:

1. Università degli Studi del Sannio, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600160.3605031

Reference42 articles.

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3. B. Biggio I. Corona G. Fumera G. Giacinto and F. Roli. 2011. Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks. In Multiple Classifier Systems C. Sansone J. Kittler and F. Roli (Eds.). Springer 350–359. B. Biggio I. Corona G. Fumera G. Giacinto and F. Roli. 2011. Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks. In Multiple Classifier Systems C. Sansone J. Kittler and F. Roli (Eds.). Springer 350–359.

4. N. Carlini and D. Wagner . 2017. Towards Evaluating the Robustness of Neural Networks . In Proc. Symposium on Security and Privacy. IEEE, 39–57 . N. Carlini and D. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In Proc. Symposium on Security and Privacy. IEEE, 39–57.

5. Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study

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