Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Yang, Zaikai; Lu, Qian; Zhang, Hanlin; Chen, Fei; Xian, Hequn

doi:10.1109/jiot.2023.3345378

IEEE Internet Things J.

2024

DOI: 10.1109/jiot.2023.3345378

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Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Zaikai Yang,

Qian Lu,

Hanlin Zhang

et al.

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2024

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Cited by 2 publications

References 46 publications

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Enhancing 5G Spectrum Sensing: Noncoherent Neyman–Pearson Detector Design and Interference Suppression Method

Jiang,

Hu,

Zhang

et al. 2024

IEEE Trans. Instrum. Meas.

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Enhancing 5G Spectrum Sensing: Noncoherent Neyman–Pearson Detector Design and Interference Suppression Method

Jiang,

Hu,

Zhang

et al. 2024

IEEE Trans. Instrum. Meas.

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Crippling Connectivity : Unveiling the Network Disruption Potential of Vehicular Rogue Access Points

Dr. Kashyap C. Patel,

Dr. Sachin A. Goswami,

Dr. Saurabh A. Dave

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Vehicular Networks have transformed modern transportation by facilitating communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Nevertheless, this technical progress brings forth new security risks, including Vehicular Rogue Access Points (VRAPs), which have the potential to disrupt network connectivity and jeopardize the integrity of vehicular networks. This research investigates the menace presented by VRAPs, analyzing their capacity to cause significant disruptions to computer networks. We conduct a thorough analysis to emphasize the methods by which VRAPs can be utilized, the many types of assaults they can enable, and the subsequent consequences for the security of vehicular networks. The results of our research highlight the immediate requirement for strong security measures to protect vehicular network from these types of risks.

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Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Cited by 2 publications

References 46 publications

Enhancing 5G Spectrum Sensing: Noncoherent Neyman–Pearson Detector Design and Interference Suppression Method

Enhancing 5G Spectrum Sensing: Noncoherent Neyman–Pearson Detector Design and Interference Suppression Method

Crippling Connectivity : Unveiling the Network Disruption Potential of Vehicular Rogue Access Points

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