基于深度信念网络的端信息跳变模式自适应策略

Research on Adaptive Strategy of End Hopping System Based on Deep Belief Nets

针对端信息跳变主动防御技术中跳变策略单一的问题,将多样异构的跳变模式引入端信息跳变系统,拓展了端信息的定义,并给出跳变策略的自适应调整方案.提出一种基于深度信念网络的端信息跳变自适应模型,形式化地描述了模型中数据收集、特征提取和状态预测等过程,定义了端信息跳变网络状态特征指标,并建立了特征数据集.利用深度信念网络对数据集进行建模,利用马尔可夫链预测下一周期的网络状态,并根据预测结果选取异构的跳变模式,从而实现端信息跳变模式的自适应变化.实验结果显示,模型网络状态识别和预测具有较高的准确性,并且异构的跳变策略能够有效抵御不同的攻击类型,进而验证了端信息跳变自适应模型的有效性和安全性.

For problem of single hopping strategy in active cyber defense of end hopping,the multiple and heterogeneous hopping patterns are introduced into the end hopping system,the definition of end information is expanded,and the self-adaptive adjustment scheme of the end hopping is given.Further,an adaptive model of end hopping based on deep belief network is proposed,and the process of data collection,feature extraction and state prediction are formalized.The state feature index of end hopping network is defined and the characteristic data set is established.The Markov chain is used to predict the state of network in the next cycle.The heterogeneous hopping mode is selected according to the prediction results.Thus,the adaptive change of the end hopping mode is realized.Experiments show that the network state recognition and prediction of the model all have high accuracy,and the heterogeneous hopping strategy can effectively resist different types of attacks,which illustrates the validity and security of the end hopping adaptive model.