ID 原文 译文
58108 针对无线传感器网络中感知节点异常状态检测困难问题，提出了一种基于感知节点任务转移概率的节点状态特征描述方式，利用该特征判断感知节点的运行状态，实现节点异常检测. The anomaly detection of sensor nodes is a great challenge to wireless sensor networks. A feature based on task transition probability is therefore proposed to model running states of sensor nodes，andthe feature can be further used for anomaly detection.
58109 基于任务转移概率的异常检测方法( T2PAD) ，根据感知节点运行任务的一步转移概率特征，对节点的运行状态进行分析，通过对转移概率向量相似性进行异常检测，识别出导致异常的任务，缩小并定位异常范围，为修正异常提供依据. Task transition probability based anomaly detection( T2PAD) analyzes states of sensor nodes based on the one-step transition probability of running taskswithin the nodes，and then performs anomaly detection by comparing similarities between transition probability vectors. T2PAD can identify tasks that caused the anomaly and narrow down the scope of problematic code，which provides clues to deal with the anomaly.
58110 传感器网络开源代码库中的缺陷实例验证了 T2PAD 对于异常检测的有效性. Case studies on defects from a sensor networkopen source project are carried out to verify the effectiveness of T2PAD.
58111 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. For problem of single hopping strategy in active cyber defense of end hopping，the multipleand 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.
58112 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. Further，anadaptive 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.
58113 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. The Markov chain is used to predict thestate of network in the next cycle. The heterogeneous hopping mode is selected according to the predictionresults. Thus，the adaptive change of the end hopping mode is realized.
58114 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 Experiments show that the network state recognition and prediction of the model all have high accuracy，and the heterogeneous hoppingstrategy can effectively resist different types of attacks，which illustrates the validity and security of theend hopping adaptive model.
58115 针对通信内容未知且无关通联占比高情况下信息传播模式的挖掘问题，提出了一个生成模型，对通联行为发生的时间建模，预测网络中用户通信内容的相关性，进而获取网络中信息的传播模式. To deal with the challenges of information diffusion pattern mining problem which the communication content is unknown and innocent data occupies a very high ratio of the observed data，the articleproposes a probability model predicting the relativity of the communications between users，which infersthe information diffusion.
58116 证明了求解所提模型的复杂度为 NP-hard，并提出用 NetMine 算法来估计模型的一个近似最优解. In addition，it proves the inferring problem NP-hard，and proposes NetMine algorithm to get a near optimal solution.
58117 实验结果表明，所提 NetMine 算法能够高效地挖掘网络中信息的传播模式，并优于已知的其他方法. Experiments show that the proposed NetMine algorithm outperformsother state-of-art algorithms.