  The developments of satellite communication in network systems require strong
and effective security plans. Attacks such as denial of service (DoS) can be
detected through the use of machine learning techniques, especially under
normal operational conditions. This work aims to provide an interruption
detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using
deep learning algorithms. Both the training, and the testing of the proposed
models are carried out with our own communication datasets, created by
utilizing a satellite traffic (benign and malicious) that was generated using
satellite networks simulation platforms, Omnet++ and Inet. We test different
deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional
Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units
(GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and
investigation of detection rate in both binary classification, and
multi-classes classification that includes different interruption categories
such as Distributed DoS (DDoS), Network Jamming, and meteorological
disturbances. Simulation results for both classification types surpassed 99.33%
in terms of detection rate in scenarios of full network surveillance. However,
in more realistic scenarios, the best-recorded performance was 96.12% for the
detection of binary traffic and 94.35% for the detection of multi-class traffic
with a false positive rate of 3.72%, using a hybrid model that combines MLP and
GRU. This Deep Learning approach efficiency calls for the necessity of using
machine learning methods to improve security and to give more awareness to
search for solutions that facilitate data collection in LEO satellite networks.

Cryptography and Security;Machine Learning