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Parallel Deep Neural Network for Detecting Computer Attacks in Information Telecommunication Systems

机译:并行深度神经网络,用于检测信息通信系统中的计算机攻击

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The approach to parallelization of the deep neural network by dividing the training set into sub-set and training each sub-set into a separate copy of the model of the neural network, which allows to significantly reduce the training time and increase the reliability of the detection of attacks, is proposed. The structure of the neural network in the framework Caffe is developed, and experimental studies have been carried out that showed an increase in the reliability of the detection of attacks in comparison with known approaches.
机译:通过将训练集划分为子集并将每个子集训练为神经网络模型的单独副本来进行深度神经网络并行化的方法,该方法可显着减少训练时间并增加神经网络模型的可靠性。建议检测攻击。开发了Caffe框架中的神经网络结构,并进行了实验研究,结果表明,与已知方法相比,攻击检测的可靠性得到了提高。

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