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AD-IoT: Anomaly Detection of IoT Cyberattacks Smart City Using Machine Leaming


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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 loT systems is growing exponentially and introducing new cybersecurity challenges since these loT 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 loT devices. In this paper, to address the loT cybersecurity threats in a smart city, we propose an Anomaly DetectionloT (AD-IoT) system, which is an intelligent anomaly detection based on Random Forest machine learning algorithm. The proposed solution can effectively detect compromised loT 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-loT can effectively achieve highest classification accuracy of 99.34% with lowest false positive rate.

KeyWords
Internet of Things (IoT), smart city, Network based IDS (NIDS), Random Forest, fog layer, IoT botnet, cybersecurity



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