The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning
Abstract
In recent years, the wide adoption of the modern Internet of Things (IoT) paradigm has led to the invention of smart cities. Smart cities operate in real-world time to promote ease and quality of life in urban cities. The network traffic of a smart city via IoT systems is growing exponentially and introducing new cybersecurity challenges since these IoT devices are being connected to sensors that are directly connected to massive cloud servers. In order to mitigate these cyberattacks, the developers need to enhance new techniques for detecting infected IoT devices. In this paper, to address the IoT cybersecurity threats in a smart city, we propose an Anomaly Detection-IoT (AD-IoT) system, which is an intelligent anomaly detection based on Random Forest machine learning algorithm. The proposed solution can effectively detect compromised IoT devices at distributed fog nodes. To evaluate our model, we utilized modern dataset to illustrate the model's accuracy. Our findings show that the AD-IoT can effectively achieve highest classification accuracy of 99.34% with lowest false positive rate.
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Citations
Cites
Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking
Restuccia, F., D'Oro, S., & Melodia, T. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2846040 · Cited by 286
Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods
Tavallaee, M., Stakhanova, N., & Ghorbani, A. A. (2010) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2048428 · Cited by 280
Cited by
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Ghimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 478
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022) · Electronics · doi:10.3390/electronics11020198 · Cited by 361
Deep learning-enabled anomaly detection for IoT systems
Abusitta, A., de Carvalho, G. H. S., Wahab, O. A., Halabi, T., Fung, B. C. M., & Mamoori, S. A. (2023) · Internet of Things · doi:10.1016/j.iot.2022.100656 · Cited by 184
Metadata
- Authors
- Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., & Ming, H.
- Venue
- 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
- Publisher
- IEEE
- Published
- 2019-01
- Pages
- 0305-0310
- OpenAlex
- W2922140331
- Cited by
- 307
- References
- 2
- FWCI
- 35.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., & Ming, H. (2019). AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). https://doi.org/10.1109/ccwc.2019.8666450
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