Abstract
Fast and reliable identification of cyber attacks in network systems of smart cities is currently a critical and demanding task. Machine learning algorithms have been used for intrusion detection, but the existing data sets intended for their training are often imbalanced, which can reduce the effectiveness of the proposed model. Oversampling and undersampling techniques can solve the problem but have limitations, such as the risk of overfitting and information loss. Furthermore, network data logs are noisy and inconsistent, making it challenging to capture essential patterns in the data accurately. To address these issues, this study proposes using Generative Adversarial Networks to generate synthetic network traffic data. The results offer new insight into developing more effective intrusion detection systems, especially in the context of smart cities’ network infrastructure.
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Acknowledgement
The financial support of the project “Application of Artificial Intelligence for Ensuring Cyber Security in Smart City", n. VJ02010016, granted by the Ministry of the Interior of the Czech Republic is gratefully acknowledged.
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Čech, P. et al. (2024). Generating Synthetic Data to Improve Intrusion Detection in Smart City Network Systems. In: Bouzefrane, S., Banerjee, S., Mourlin, F., Boumerdassi, S., Renault, É. (eds) Mobile, Secure, and Programmable Networking. MSPN 2023. Lecture Notes in Computer Science, vol 14482. Springer, Cham. https://doi.org/10.1007/978-3-031-52426-4_3
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DOI: https://doi.org/10.1007/978-3-031-52426-4_3
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