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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An intrusion detection system based on an FNN performs better than with SNN on the BoT-IoT dataset.
“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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Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches
Khaleel, Y. L., Habeeb, M. A., & Alnabulsi, H. (2024) · Applied Data Science and Analysis · doi:10.58496/adsa/2024/011 · Cited by 37 · see citing works
Adversarial Attacks Against IoT Networks using Conditional GAN based Learning
Benaddi, H., Jouhari, M., Ibrahimi, K., Benslimane, A., & Amhoud, E. M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000726 · Cited by 37 · see citing works
Adversarial Training for Deep Learning-based Intrusion Detection SystemsPreprint
Debicha, I., Debatty, T., Dricot, J., & Mees, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09852 · Cited by 37 · see citing works
Hybrid deep learning based threat intelligence framework for Industrial IoT systems
Malik, J., Akhunzada, A., Al‐Shamayleh, A. S., Zeadally, S., & Almogren, A. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100846 · Cited by 35 · see citing works
Intrusion Detection System for IoHT Devices using Federated Learning
Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Han, M., & Batista, D. M. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10225932 · Cited by 33 · see citing works
Meziane, H., & Ouerdi, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-46640-9 · Cited by 32 · see citing works
AppCon: Mitigating Evasion Attacks to ML Cyber Detectors
Apruzzese, G., Andreolini, M., Marchetti, M., Colacino, V. G., & Russo, G. (2020) · Symmetry · doi:10.3390/sym12040653 · Cited by 32 · see citing works
Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review
Kimbugwe, N., Pei, T., & Kyebambe, M. N. (2021) · Energies · doi:10.3390/en14196384 · Cited by 32 · see citing works
Machine and deep learning amalgamation for feature extraction in Industrial Internet-of-Things
Jayalaxmi, P. L. S., Saha, R., Kumar, G., & Kim, T.-H. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107610 · Cited by 32 · see citing works
Security of Machine Learning-Based Anomaly Detection in Cyber Physical Systems
Jadidi, Z., Pal, S., Nithesh Nayak, K., Selvakkumar, A., Chang, C.-C., Beheshti, M., & Jolfaei, A. (2022) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn54977.2022.9868845 · Cited by 32 · see citing works
TransfficFormer: A novel Transformer-based framework to generate evasive malicious traffic
Du, W., Xue, J., Yang, X., Guo, W., Gu, D., & Han, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113546 · Cited by 31 · see citing works
Pandey, V. K., Prakash, S., Gupta, T. K., Sinha, P., Yang, T., Rathore, R. S., Wang, L., Tahir, S., & Bakhsh, S. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-03498-3 · Cited by 31 · see citing works
Singh, A., & Sikdar, B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3138541 · Cited by 31 · see citing works
Development of a Data-Driven Mobile 5G Testbed: Platform for Experimental Research
Wang, Y., Górski, A., & Silva, A. P. (2021) · IEEE International Mediterranean Conference on Communications and Networking (MeditCom) · doi:10.1109/meditcom49071.2021.9647461 · Cited by 30 · see citing works
Rehman, H. M. R. U., Liaquat, S., Gul, M. J., Jhandir, M. Z., Aray, D. G., Vergara, M. M., & Ashraf, I. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01323-2 · Cited by 29 · see citing works
Alaghbari, K. A., Saad, M. H. M., Hussain, A., & Alam, M. R. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00338-x · Cited by 28 · see citing works
Ibitoye, O., Shafiq, M. O., & Matrawy, A. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102631 · Cited by 27 · see citing works
Self-citationCrafting Adversarial Samples for Anomaly Detectors in Industrial Control Systems
Gómez, Á. L. P., Maimó, L. F., Celdrán, A. H., Clemente, F. J. G., & Cleary, F. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.03.072 · Cited by 27 · see citing works
EIoT-DDoS: embedded classification approach for IoT traffic-based DDoS attacks
Shukla, P. K., Krishna, C. R., & Patil, N. V. (2023) · Cluster Computing · doi:10.1007/s10586-023-04027-5 · Cited by 26 · see citing works
- 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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