Joint BP and RNN for Resilient GPS Timing Against Spoofing Attacks

    •  Bhamidipati, S., Kim, K.J., Sun, H., Orlik, P.V., Zhang, J., "Joint BP and RNN for Resilient GPS Timing Against Spoofing Attacks" in Social Informatics and Telecommunications Engineering, Han S., Ye L., Meng W., Eds., DOI: 10.1007/978-3-030-22971-9_17, vol. 287 of Lecture Notes of the Institute for Computer Sciences, Springer, Cham, November 2019.
      BibTeX Download PDF
      • @incollection{Bhamidipati2019nov,
      • author = {Bhamidipati, Sriramya and Kim, Kyeong Jin and Sun, Hongbo and Orlik, Philip V. and Zhang, Jinyun},
      • title = {Joint BP and RNN for Resilient GPS Timing Against Spoofing Attacks},
      • booktitle = {Social Informatics and Telecommunications Engineering},
      • year = 2019,
      • editor = {Han S., Ye L., Meng W.},
      • volume = 287,
      • series = {Lecture Notes of the Institute for Computer Sciences},
      • month = nov,
      • publisher = {Springer, Cham},
      • doi = {10.1007/978-3-030-22971-9_17},
      • url = {}
      • }
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  • Research Areas:

    Artificial Intelligence, Communications, Machine Learning, Signal Processing

In this paper, we propose a new wide-area algorithm to secure the Global Positioning System (GPS) timing from spoofing attack. To achieve a trusted GPS timing, belief propagation (BP), recognized as one of the Artificial Intelligence (AI) approaches, and the recurrent neural network (RNN) are jointly integrated. BP is employed to authenticate each GPS receiving system in the wide-area network from malicious spoofing attacks and estimate the corresponding spoofing-induced timing error. To evaluate the spoofing status at each of the GPS receiving system, RNN is utilized to evaluate similarity in spoofinginduced errors across the antennas within the GPS receiving system. Having applied a proper training stage, simulation results show that the proposed joint BP-RNN algorithms can quickly detect the spoofed receiving system comparing with existing work.