@inproceedings{b7101893e2f749988419ea376385f838,
title = "Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks",
abstract = "This paper proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was trained for frequency hopping sequences that use maximum-length sequences that utilize minimal polynomials as the primitive polynomial used in the linear-shift feedback register. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with as few as 4 jammer hopping samples. The model is also capable of determining if the jammer is utilizing a sequence that the model is trained for in as few as 25 jammer hopping samples.",
keywords = "Artificial Intelligence, Frequency Hopping, Jamming, Maximum Length Sequences, Neural Network, Pseudo Noise, Random Number Generators",
author = "Strickland, {Charles J.} and Haddad, {Rami J.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE SoutheastCon, SoutheastCon 2023 ; Conference date: 01-04-2023 Through 16-04-2023",
year = "2023",
doi = "10.1109/SoutheastCon51012.2023.10115067",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "313--318",
booktitle = "SoutheastCon 2023",
address = "United States",
}