The DOI matches its Crossref record; 20 of the 22 references it cites were found in a registry or an index.
Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks
Open accessAbstract
Adversarial attacks have been widely studied in the field of computer vision but their impact on network security applications remains an area of open research. As IoT, 5G and AI continue to converge to realize the promise of the fourth industrial revolution (Industry 4.0), security incidents and events on IoT networks have increased. Deep learning techniques are being applied to detect and mitigate many of such security threats against IoT networks. Feed- forward Neural Networks (FNN) have been widely used for classifying intrusion attacks in IoT networks. In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network. Our analysis is performed using the BoT- IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. In our experimental results, the FNN outperforms the SNN for intrusion detection in IoT networks based on multiple performance metrics such as accuracy, precision, and recall as well as multi-classification metrics such as Cohen Cappa’s score. However, when tested for adversarial robustness, the SNN demonstrates better resilience against the adversarial samples from the IoT dataset, presenting a promising future in the quest for safer and more secure deep learning in IoT networks.
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“The performance results on the BoT-IoT dataset show that an intrusion detection system based on an FNN performs better than with SNN.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [121]
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Cites
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602
The Limitations of Deep Learning in Adversarial Settings
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., & Bengio, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01236 · Cited by 2,325
Self-Normalizing Neural Networks
Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.02515 · Cited by 2,267
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Research works printed but not linked: 2
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Sorted by citations
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey
He, K., Kim, D. S., & Asghar, M. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3233793 · Cited by 518
Tange, K., De Donno, M., Fafoutis, X., & Dragoni, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011208 · Cited by 410
“In [186], the authors show that one can reliably create adversarial samples that defeat deep learning based systems.”
BackgroundFederated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295
Metadata
- Authors
- Ibitoye, O., Shafiq, O., & Matrawy, A.
- Affiliations
- Carleton University (CA)
- Venue
- 2019 IEEE Global Communications Conference (GLOBECOM)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 1-6
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W2944645079
- Cited by
- 281
- References
- 22
- Citations by year
- 2026: 24 · 2025: 40 · 2024: 45 · 2023: 44 · 2022: 54 · 2021: 55 · 2020: 15 · 2019: 4
- FWCI
- 27.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Globecom (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- adversarial attacks, adversarial robustness, intrusion detection, Internet of Things security, self-normalizing neural networks, feedforward neural networks, Bot-IoT dataset, deep learning security
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