| Title | A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City |
|---|---|
| ID_Doc | 36837 |
| Authors | Elsaeidy, AA; Jamalipour, A; Munasinghe, KS |
| Title | A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City |
| Year | 2021 |
| Published | |
| Abstract | Today's smart city infrastructure is predominantly dependant on Internet of Things (IoT) technologies. IoT technology essentially facilitates a platform for service automation through connections of heterogeneous objects via the Internet backbone. However, the security issues associated with IoT networks make smart city infrastructure vulnerable to cyber-attacks. For example, Distributed Denial of Service (DDoS) attack violates the authorization conditions in smart city infrastructure; whereas replay attack violates the authentication conditions in smart city infrastructure. Both attacks lead to physical disruption to smart city infrastructure, which may even lead to financial loss and/or loss of human lives. In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real life smart city platform. The performance of the proposed hybrid model is evaluated using real life smart city datasets (environmental, smart river and smart soil), where DDoS and replay attacks were simulated. The proposed model reported high accuracy rates: 98.37% for the environmental dataset, 98.13% for the smart river dataset, and 99.51% for the smart soil dataset. The results demonstrated an improved performance of the proposed model over other machine learning and deep learning models from the literature. |
| https://doi.org/10.1109/access.2021.3128701 |
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