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Title	Securing Smart Cities: Ai-Driven Video Injection Attack Detection For Enhanced Urban Surveillance
ID_Doc	47788
Authors	Bhardwaj A.; Ojha S.S.; Dubey R.
Year	2024
Published	Proceedings - 2024 International Conference on Social and Sustainable Innovations in Technology and Engineering, SASI-ITE 2024
DOI	http://dx.doi.org/10.1109/SASI-ITE58663.2024.00014
Abstract	With the integration of video surveillance sensors into smart city applications, video content security becomes more important. This research uses a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to provide a detailed framework to detect video injection attacks. The method is evaluated using the Kitsune Network Attack Dataset, which includes a variety of network traffic scenarios for an in-depth examination. The proposed model achieves excellent outcomes with an accuracy of 98.0%, precision of 99.8%, recall of 99.7%, and F1-score of 99.6%. Moreover, extensive examination using a confusion matrix and metrics such as False Discovery Rate (FDR), False Negative Rate (FNR), False Omission Rate (FOR), and False Positive Rate (FPR) illustrate the model's reliability in identifying between genuine and injected video and audio frames. The successful implementation of the proposed video injection attack detection technique has been demonstrated in this paper, which is facilitating the improvement of smart city security. The results of this research paper provide significant perspectives for implementing forefront safety procedures and preserving the reliability and integrity of video frames in the context of smart cities. © 2024 IEEE.
Author Keywords	Artificial Intelligence; Infrastructure Security; Network Security; Smart City; Urban Surveillance; Video Injection Attack

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