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Title	Generating Synthetic Data To Improve Intrusion Detection In Smart City Network Systems
ID_Doc	27811
Authors	Čech P.; Ponce D.; Mikulecký P.; Mls K.; Žváčková A.; Tučník P.; Otčenášková T.
Year	2024
Published	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14482 LNCS
DOI	http://dx.doi.org/10.1007/978-3-031-52426-4_3
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Author Keywords	generative adversarial networks; imbalanced datasets; intrusion detection; smart cities

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