目前已有一些全球化的网络蠕虫监测方法,但这些方法并不能很好地适用于局域网.为此,提出一种使用本地网协同检测蠕虫的方法CWDMLN(coordinated worm detection method based on local nets).CWDMLN注重分析扫描蠕虫在本地网的行为,针对...目前已有一些全球化的网络蠕虫监测方法,但这些方法并不能很好地适用于局域网.为此,提出一种使用本地网协同检测蠕虫的方法CWDMLN(coordinated worm detection method based on local nets).CWDMLN注重分析扫描蠕虫在本地网的行为,针对不同的行为特性使用不同的处理方法,如蜜罐诱捕.通过协同这些方法给出预警信息,以揭示蠕虫在本地网络中的活动情况.预警信息的级别反映报警信息可信度的高低.实验结果表明,该方法可以准确、快速地检测出入侵本地网络的扫描蠕虫,其抽取出的蠕虫行为模式可以为协同防御提供未知蠕虫特征.通过规模扩展,能够实施全球网络的蠕虫监控.展开更多
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
文摘目前已有一些全球化的网络蠕虫监测方法,但这些方法并不能很好地适用于局域网.为此,提出一种使用本地网协同检测蠕虫的方法CWDMLN(coordinated worm detection method based on local nets).CWDMLN注重分析扫描蠕虫在本地网的行为,针对不同的行为特性使用不同的处理方法,如蜜罐诱捕.通过协同这些方法给出预警信息,以揭示蠕虫在本地网络中的活动情况.预警信息的级别反映报警信息可信度的高低.实验结果表明,该方法可以准确、快速地检测出入侵本地网络的扫描蠕虫,其抽取出的蠕虫行为模式可以为协同防御提供未知蠕虫特征.通过规模扩展,能够实施全球网络的蠕虫监控.
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.
