Aiming at the difficulty of unknown Trojan detection in the APT flooding situation, an improved detecting method has been proposed. The basic idea of this method originates from advanced persistent threat （APT） atta...Aiming at the difficulty of unknown Trojan detection in the APT flooding situation, an improved detecting method has been proposed. The basic idea of this method originates from advanced persistent threat （APT） attack intents： besides dealing with damaging or destroying facilities, the more essential purpose of APT attacks is to gather confidential data from target hosts by planting Trojans. Inspired by this idea and some in-depth analyses on recently happened APT attacks, five typical communication characteristics are adopted to describe application’s network behavior, with which a fine-grained classifier based on Decision Tree and Na ve Bayes is modeled. Finally, with the training of supervised machine learning approaches, the classification detection method is implemented. Compared with general methods, this method is capable of enhancing the detection and awareness capability of unknown Trojans with less resource consumption.展开更多
A new approach of adaptive distributed control is proposed for a class of networks with unknown time-varying coupling weights. The proposed approach ensures that the complex dynamical networks achieve asymptotical syn...A new approach of adaptive distributed control is proposed for a class of networks with unknown time-varying coupling weights. The proposed approach ensures that the complex dynamical networks achieve asymptotical synchronization and all the closed-loop signals are bounded. Furthermore, the coupling matrix is not assumed to be symmetric or irreducible and asymptotical synchronization can be achieved even when the graph of network is not connected. Finally, a simulation example shows the feasibility and effectiveness of the approach.展开更多
This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis func...This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability.The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded（SGUUB） and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme.展开更多
针对传统工控网络入侵检测算法无法识别未知威胁与细粒度分类的问题,提出一种工业控制网络未知威胁识别与学习方法。该方法通过单类与多类支持向量机构建SVM融合策略,检测未知威胁并划分已知类别;建立改进的深度嵌入K均值聚类模型IDEKC(...针对传统工控网络入侵检测算法无法识别未知威胁与细粒度分类的问题,提出一种工业控制网络未知威胁识别与学习方法。该方法通过单类与多类支持向量机构建SVM融合策略,检测未知威胁并划分已知类别;建立改进的深度嵌入K均值聚类模型IDEKC(improved deep embedded k-means clustering)对未知威胁细粒度分类,同时引入增量学习模型学习新类别。在密西西比州立大学天然气管道数据集和实验室油气集输靶场实验,实验结果表明未知威胁平均检测率分别达97.65%、92.95%,细粒度分类平均准确率分别为91.22%、91.24%,验证了该方法在未知威胁检测上准确性高且泛化能力强。展开更多
基金Supported by the National Natural Science Foundation of China (61202387, 61103220)Major Projects of National Science and Technology of China(2010ZX03006-001-01)+3 种基金Doctoral Fund of Ministry of Education of China (2012014110002)China Postdoctoral Science Foundation (2012M510641)Hubei Province Natural Science Foundation (2011CDB456)Wuhan Chenguang Plan Project(2012710367)
文摘Aiming at the difficulty of unknown Trojan detection in the APT flooding situation, an improved detecting method has been proposed. The basic idea of this method originates from advanced persistent threat （APT） attack intents： besides dealing with damaging or destroying facilities, the more essential purpose of APT attacks is to gather confidential data from target hosts by planting Trojans. Inspired by this idea and some in-depth analyses on recently happened APT attacks, five typical communication characteristics are adopted to describe application’s network behavior, with which a fine-grained classifier based on Decision Tree and Na ve Bayes is modeled. Finally, with the training of supervised machine learning approaches, the classification detection method is implemented. Compared with general methods, this method is capable of enhancing the detection and awareness capability of unknown Trojans with less resource consumption.
基金supported by Ph.D.Programs Foundation of Ministry of Education of China(Nos.JY0300137002 and20130203110021)Research Funds for the Central Universities(No.JB142001-6)
文摘A new approach of adaptive distributed control is proposed for a class of networks with unknown time-varying coupling weights. The proposed approach ensures that the complex dynamical networks achieve asymptotical synchronization and all the closed-loop signals are bounded. Furthermore, the coupling matrix is not assumed to be symmetric or irreducible and asymptotical synchronization can be achieved even when the graph of network is not connected. Finally, a simulation example shows the feasibility and effectiveness of the approach.
基金supported by National Natural Science Foundation of China(Nos.61573172,61305149 and 61403174)333 High-level Talents Training Program in Jiangsu Province(No.BRA2015352)Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(No.15KJB510011)
文摘This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability.The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded（SGUUB） and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme.
文摘针对传统工控网络入侵检测算法无法识别未知威胁与细粒度分类的问题,提出一种工业控制网络未知威胁识别与学习方法。该方法通过单类与多类支持向量机构建SVM融合策略,检测未知威胁并划分已知类别;建立改进的深度嵌入K均值聚类模型IDEKC(improved deep embedded k-means clustering)对未知威胁细粒度分类,同时引入增量学习模型学习新类别。在密西西比州立大学天然气管道数据集和实验室油气集输靶场实验,实验结果表明未知威胁平均检测率分别达97.65%、92.95%,细粒度分类平均准确率分别为91.22%、91.24%,验证了该方法在未知威胁检测上准确性高且泛化能力强。
