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Risk Assessment Method for Aviation ATM Network Based on RBF Neural Network

机译:基于RBF神经网络的航空ATM网络风险评估方法

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ATM (Asynchronous Transfer Mode) network is the core communication network of civil aviation aeronautical telecommunication network. So it is an urgent time to do scientific risk assessment for ATM network as soon as possible. According to threats and vulnerabilities existing in ATM network, which could bring bad influence to assets and missions of ATM network, even threaten the whole security situation of ATM network. This paper proposes risk assessment model based on RBF neural network. According to the established evaluation model, indexes that influencing the security situation of missions are used as input of the model and train the model. The well-trained neural network model is used to assess ATM network, while the results are compared to that of the traditional methods of scoring by experts for rounds of times. The experimental results demonstrate that the risk assessment model has strong capacities of self-learning and convergence, accords well with the complex ATM network for risk assessment.
机译:ATM(异步传输模式)网络是民用航空航空电信网络的核心通信网络。因此,尽快为ATM网络进行科学风险评估是一项迫切的时间。根据ATM网络存在的威胁和漏洞,这可能对ATM网络的资产和特派团带来不良影响,甚至可能威胁到ATM网络的整个安全情况。本文提出了基于RBF神经网络的风险评估模型。根据既定的评估模型,影响任务安全状况的指数用作模型的输入并培训模型。训练有素的神经网络模型用于评估ATM网络,同时将结果与由专家进行回合的传统评分方法进行比较。实验结果表明,风险评估模型具有强大的自学习和收敛能力,适用于复杂的ATM网络进行风险评估。

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