We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study th...We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study the robustness of complex networks under random information and preferential information, respectively. Using the generating function method, we derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component. We show that hiding just a small fraction of nodes randomly can prevent a scale-free network from collapsing and detecting just a small fraction of nodes preferentially can destroy a scale-free network.展开更多
鲁棒性作为一种动态行为也是超网络领域的研究热点,对构建鲁棒网络具有重要的现实意义。尽管对超网络的研究越来越多,但对其动态研究相对较少,尤其是在神经影像领域。在现有的脑功能超网络研究中,大多是探究网络的静态拓扑属性,并没有...鲁棒性作为一种动态行为也是超网络领域的研究热点,对构建鲁棒网络具有重要的现实意义。尽管对超网络的研究越来越多,但对其动态研究相对较少,尤其是在神经影像领域。在现有的脑功能超网络研究中,大多是探究网络的静态拓扑属性,并没有相关研究对脑功能超网络的动力学特性——鲁棒性展开分析。针对这些问题,文中首先引入lasso,group lasso和sparse group lasso方法来求解稀疏线性回归模型以构建超网络;然后基于蓄意攻击中的节点度和节点介数攻击两种实验模型,利用全局效率和最大连通子图相对大小探究脑功能超网络在应对攻击时的节点失效网络的鲁棒性,最后通过实验进行对比分析,以探究更为稳定的网络。实验结果表明,在蓄意攻击模式下,group lasso和sparse group lasso方法构建的超网络的鲁棒性更强一些。同时,综合来看,group lasso方法构建的超网络最稳定。展开更多
Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems(ICS).These systems are becoming more connected to the internet,either directly or through the co...Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems(ICS).These systems are becoming more connected to the internet,either directly or through the corporate networks.This makes them vulnerable to cyber-attacks.Abnormal behaviour in floodgates operated by ICS could be caused by both(intentional)attacks and(accidental)technical failures.When operators notice abnormal behaviour,they should be able to distinguish between those two causes to take appropriate measures,because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector.In the previous work,we developed the attack-failure distinguisher framework for constructing Bayesian Network(BN)models to enable operators to distinguish between those two causes,including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models.As a full case study of the attack-failure distinguisher framework,this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates,addressing the problem of floodgate operators.We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model.The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements.This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 70501032.
文摘We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study the robustness of complex networks under random information and preferential information, respectively. Using the generating function method, we derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component. We show that hiding just a small fraction of nodes randomly can prevent a scale-free network from collapsing and detecting just a small fraction of nodes preferentially can destroy a scale-free network.
文摘鲁棒性作为一种动态行为也是超网络领域的研究热点,对构建鲁棒网络具有重要的现实意义。尽管对超网络的研究越来越多,但对其动态研究相对较少,尤其是在神经影像领域。在现有的脑功能超网络研究中,大多是探究网络的静态拓扑属性,并没有相关研究对脑功能超网络的动力学特性——鲁棒性展开分析。针对这些问题,文中首先引入lasso,group lasso和sparse group lasso方法来求解稀疏线性回归模型以构建超网络;然后基于蓄意攻击中的节点度和节点介数攻击两种实验模型,利用全局效率和最大连通子图相对大小探究脑功能超网络在应对攻击时的节点失效网络的鲁棒性,最后通过实验进行对比分析,以探究更为稳定的网络。实验结果表明,在蓄意攻击模式下,group lasso和sparse group lasso方法构建的超网络的鲁棒性更强一些。同时,综合来看,group lasso方法构建的超网络最稳定。
基金the Netherlands Organization for Scientific Research(NWO)in the framwork of the Cyber Security research program under the project“Secure Our Safety:Building Cyber Security for Flood Management(SOS4Flood)”.
文摘Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems(ICS).These systems are becoming more connected to the internet,either directly or through the corporate networks.This makes them vulnerable to cyber-attacks.Abnormal behaviour in floodgates operated by ICS could be caused by both(intentional)attacks and(accidental)technical failures.When operators notice abnormal behaviour,they should be able to distinguish between those two causes to take appropriate measures,because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector.In the previous work,we developed the attack-failure distinguisher framework for constructing Bayesian Network(BN)models to enable operators to distinguish between those two causes,including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models.As a full case study of the attack-failure distinguisher framework,this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates,addressing the problem of floodgate operators.We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model.The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements.This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.
