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Adversarial attacks against supervised machine learning based network intrusion detection systems

Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this resea...

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Autores principales: Alshahrani, Ebtihaj, Alghazzawi, Daniyal, Alotaibi, Reem, Rabie, Osama
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565394/
https://www.ncbi.nlm.nih.gov/pubmed/36240162
http://dx.doi.org/10.1371/journal.pone.0275971
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author Alshahrani, Ebtihaj
Alghazzawi, Daniyal
Alotaibi, Reem
Rabie, Osama
author_facet Alshahrani, Ebtihaj
Alghazzawi, Daniyal
Alotaibi, Reem
Rabie, Osama
author_sort Alshahrani, Ebtihaj
collection PubMed
description Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this research, we performed two adversarial attack scenarios, we used a Generative Adversarial Network (GAN) to generate synthetic intrusion traffic to test the influence of these attacks on the accuracy of machine learning-based Intrusion Detection Systems(IDSs). We conducted two experiments on adversarial attacks including poisoning and evasion attacks on two different types of machine learning models: Decision Tree and Logistic Regression. The performance of implemented adversarial attack scenarios was evaluated using the CICIDS2017 dataset. Also, it was based on a comparison of the accuracy of machine learning-based IDS before and after attacks. The results show that the proposed evasion attacks reduced the testing accuracy of both network intrusion detection systems models (NIDS). That illustrates our evasion attack scenario negatively affected the accuracy of machine learning-based network intrusion detection systems, whereas the decision tree model was more affected than logistic regression. Furthermore, our poisoning attack scenario disrupted the training process of machine learning-based NIDS, whereas the logistic regression model was more affected than the decision tree.
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spelling pubmed-95653942022-10-15 Adversarial attacks against supervised machine learning based network intrusion detection systems Alshahrani, Ebtihaj Alghazzawi, Daniyal Alotaibi, Reem Rabie, Osama PLoS One Research Article Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this research, we performed two adversarial attack scenarios, we used a Generative Adversarial Network (GAN) to generate synthetic intrusion traffic to test the influence of these attacks on the accuracy of machine learning-based Intrusion Detection Systems(IDSs). We conducted two experiments on adversarial attacks including poisoning and evasion attacks on two different types of machine learning models: Decision Tree and Logistic Regression. The performance of implemented adversarial attack scenarios was evaluated using the CICIDS2017 dataset. Also, it was based on a comparison of the accuracy of machine learning-based IDS before and after attacks. The results show that the proposed evasion attacks reduced the testing accuracy of both network intrusion detection systems models (NIDS). That illustrates our evasion attack scenario negatively affected the accuracy of machine learning-based network intrusion detection systems, whereas the decision tree model was more affected than logistic regression. Furthermore, our poisoning attack scenario disrupted the training process of machine learning-based NIDS, whereas the logistic regression model was more affected than the decision tree. Public Library of Science 2022-10-14 /pmc/articles/PMC9565394/ /pubmed/36240162 http://dx.doi.org/10.1371/journal.pone.0275971 Text en © 2022 Alshahrani et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Alshahrani, Ebtihaj
Alghazzawi, Daniyal
Alotaibi, Reem
Rabie, Osama
Adversarial attacks against supervised machine learning based network intrusion detection systems
title Adversarial attacks against supervised machine learning based network intrusion detection systems
title_full Adversarial attacks against supervised machine learning based network intrusion detection systems
title_fullStr Adversarial attacks against supervised machine learning based network intrusion detection systems
title_full_unstemmed Adversarial attacks against supervised machine learning based network intrusion detection systems
title_short Adversarial attacks against supervised machine learning based network intrusion detection systems
title_sort adversarial attacks against supervised machine learning based network intrusion detection systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565394/
https://www.ncbi.nlm.nih.gov/pubmed/36240162
http://dx.doi.org/10.1371/journal.pone.0275971
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