Deep Learning-Enhanced anomaly detection for IoT security in smart cities
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Full Text |
Pdf
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Author |
Tarik Himdi and Mohammed Ishaque
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e-ISSN |
1819-6608 |
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On Pages
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391-397
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Volume No. |
19
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Issue No. |
6
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Issue Date |
May 15, 2024
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DOI |
https://doi.org/10.59018/032456
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Keywords |
intrusion detection system, deep learning, LSTM, GRU, IoT, smart cities.
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Abstract
The swift expansion of Internet of Things (IoT) devices within smart cities necessitates robust security measures
to safeguard critical infrastructure and ensure citizen safety. In response, this research presents an advanced deep learning-
based anomaly detection system designed to bolster IoT security within the context of smart cities. Leveraging the IoT-23
dataset, our system demonstrates impressive results. One of the system's notable strengths is its adaptability; it generalizes
well to diverse datasets and maintains its efficacy in the presence of adversarial attacks. An intuitive user interface
facilitates system management and response to detected anomalies, providing a holistic approach to IoT security in smart
cities. Positive user feedback affirms the system's usability and satisfaction, emphasizing its practical utility. This research
contributes to the broader field of IoT security. It furnishes well-documented code and resources, laying the groundwork
for further advancements in this critical domain. As smart cities continue to evolve, the findings and innovations presented
in this research serve as a vital step toward ensuring the integrity, privacy, and reliability of IoT networks within urban
environments. Lastly, the findings of the experiments show that this technique has an excellent detection performance, with
an accuracy rate which is more than 98.7%.
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