AdIoTack: Quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks-Reference-Cited by-同舟云学术

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AdIoTack: Quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks

Published:2022-09 Issue: Volume:120 Page:102801
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Pashamokhtari Arman,Batista Gustavo,Habibi Gharakheili Hassan^ORCID

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference54 articles.

1. Adversarial learning attacks on graph-based IoT malware detection systems;Abusnaina,2019

2. PARVP: passively assessing risk of vulnerable passwords for HTTP authentication in networked cameras;Anand,2021

3. The security of machine learning;Barreno;Springer Mach. Learn.,2010

4. Bagging predictors;Breiman;Springer Mach. Learn.,1996

5. Random forests;Breiman;Springer Mach. Learn.,2001

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1. Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques;Sensors;2024-06-01

2. Cyber Resilience of Cyber-Physical Systems and Machine Learning, a Scoping Review;Lecture Notes in Networks and Systems;2024

3. Cyber Resilience Quantification: A Probabilistic Estimation Model for it Infrastructure;2024

4. Deep Ensemble-based Efficient Framework for Network Attack Detection;2023 21st Mediterranean Communication and Computer Networking Conference (MedComNet);2023-06-13

5. Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning;Sensors;2022-12-14

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