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The developments of satellite communication in network systems require strongand effective security plans. Attacks such as denial of service (DoS) can bedetected through the use of machine learning techniques, especially undernormal operational conditions. This work aims to provide an interruptiondetection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks usingdeep learning algorithms. Both the training, and the testing of the proposedmodels are carried out with our own communication datasets, created byutilizing a satellite traffic (benign and malicious) that was generated usingsatellite networks simulation platforms, Omnet++ and Inet. We test differentdeep learning algorithms including Multi Layer Perceptron (MLP), ConvolutionalNeural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units(GRU), and Long Short-term Memory (LSTM). Followed by a full analysis andinvestigation of detection rate in both binary classification, andmulti-classes classification that includes different interruption categoriessuch as Distributed DoS (DDoS), Network Jamming, and meteorologicaldisturbances. 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 thedetection of binary traffic and 94.35% for the detection of multi-class trafficwith a false positive rate of 3.72%, using a hybrid model that combines MLP andGRU. This Deep Learning approach efficiency calls for the necessity of usingmachine learning methods to improve security and to give more awareness tosearch for solutions that facilitate data collection in LEO satellite networks.

Deep learning approach for interruption attacks detection in LEO satellite networks