Utilizing Deep Learning in Smart Cities for Environmental Monitoring

The effectiveness and dependability of services provided by smart cities are significantly impacted by the significant and difficult issue of intrusion detection. Replay attacks, which weaken security via improper authentication in a smart city network, are among the most common threats to this infrastructure. For example, replay attacks have the potential to severely damage the smart city structure, leading to the loss of crucial data and substantial monetary losses. As a result, we have developed a model based on deep learning to predict where attacks in smart cities would be replayed. The proposed approach is intriguing since it uses deep learning-based models as an application to differentiate replay assaults and increase recognition accuracy. Replay attacks are simulated on a real-world smart city dataset, and the model's performance is assessed by using this dataset. Our findings demonstrate that the suggested model may reasonably accurately discriminate between normal and assault actions. Furthermore, our suggested model performs better than both deep learning and conventional classification models, per the findings.