When users exchange data with Unmanned Aerial Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e., data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a Deep Learning (DL) approach for detecting attacks on UAVs equipped with Orthogonal Frequency Division Multiplexing - (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The developed algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks shows that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. The developed algorithm also detects moving attackers from a distance of 500 m.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Convolutional Attention Based Deep Learning Solution for 5G UAV Network Attack Recognition over Fading Channels and Interference


    Contributors:


    Publication date :

    2022-09-01


    Size :

    822314 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    AN ATTENTION-BASED RECURRENT CONVOLUTIONAL NETWORK FOR VEHICLE TAILLIGHT RECOGNITION

    Lee, Kuan-Hui / Tagawa, Takaaki / Pan, Jia-En M. et al. | British Library Conference Proceedings | 2019


    An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

    Lee, Kuan-Hui / Tagawa, Takaaki / Pan, Jia-En M. et al. | IEEE | 2019


    On Network Coding Design for URLLC over Fading Channels

    Choi, Jinho / Nemati, Mahyar | IEEE | 2022


    Computer generated hologram compression with attention-based deep convolutional neural network

    Shen, Zhelun / Yang, Guanglin / Xie, Haiyan | British Library Conference Proceedings | 2021