共享网络不协调目标信息脆弱性风险评估仿真
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摘要
当前网络目标信息脆弱性风险评估方法评估准确性差、评估耗时长,提出基于关联图的共享网络中不协调目标信息脆弱性风险评估方法。对网络中含有的噪声信号实行DWT处理,获取第一尺度上低频系数、高频系数。对噪声信号进行平移,获取一个新信号,对新信号实行DWT处理,并给出新信号第一尺度上低频系数、高频系数。对所有尺度上的低频系数和高频系数进行阈值处理,得到各个尺度上低频系数和高频系数估计值。对所得值进行小波逆变换,完成共享网络中不协调目标信息传输信道去噪。根据去噪结果,利用共享网络中的节点集合、网络安全关系模型、脆弱性模型找到共享网络中全部可能攻击。将攻击者集合作为起点,对原子攻击进行搜索以生成攻击链,借此完成关联图模型的构建。根据目标信息脆弱性风险关联图,实现目标信息脆弱性风险评估模型的构建。仿真表明,所提方法评估结果与实际结果拟合度较高、评估耗时短,可为该领域发展提供借鉴作用。
The current network target information vulnerability risk assessment method has poor evaluation accuracy and long evaluation time. A risk assessment method for uncoordinated target information vulnerability in shared networks based on association graph is proposed. DWT processing is performed on the noise signal contained in the network to obtain low frequency coefficients and high frequency coefficients on the first scale. The noise signal is translated,a new signal is obtained,DWT processing is performed on the new signal,and the low frequency coefficient and the high frequency coefficient of the first signal of the new signal are given. Threshold processing is performed on low-frequency coefficients and high-frequency coefficients on all scales to obtain low-frequency coefficients and high-frequency coefficient estimates at various scales. The inverse wavelet transform is performed on the obtained value to complete the denoising of the uncoordinated target information transmission channel in the shared network. According to the denoising result,all possible attacks in the shared network are found by using the node set,the network security relationship model and the vulnerability model in the shared network. The attacker set is used as a starting point to search for atomic attacks to generate an attack chain,thereby completing the construction of the association graph model. According to the target information vulnerability risk association map,the target information vulnerability risk assessment model is constructed. The simulations show that the evaluation results of the proposed method are more suitable for the actual results and the evaluation time is short,which can provide reference for the development of the field.
The current network target information vulnerability risk assessment method has poor evaluation accuracy and long evaluation time. A risk assessment method for uncoordinated target information vulnerability in shared networks based on association graph is proposed. DWT processing is performed on the noise signal contained in the network to obtain low frequency coefficients and high frequency coefficients on the first scale. The noise signal is translated,a new signal is obtained,DWT processing is performed on the new signal,and the low frequency coefficient and the high frequency coefficient of the first signal of the new signal are given. Threshold processing is performed on low-frequency coefficients and high-frequency coefficients on all scales to obtain low-frequency coefficients and high-frequency coefficient estimates at various scales. The inverse wavelet transform is performed on the obtained value to complete the denoising of the uncoordinated target information transmission channel in the shared network. According to the denoising result,all possible attacks in the shared network are found by using the node set,the network security relationship model and the vulnerability model in the shared network. The attacker set is used as a starting point to search for atomic attacks to generate an attack chain,thereby completing the construction of the association graph model. According to the target information vulnerability risk association map,the target information vulnerability risk assessment model is constructed. The simulations show that the evaluation results of the proposed method are more suitable for the actual results and the evaluation time is short,which can provide reference for the development of the field.
引文
[1]王雪丽,等.面向可信软件的风险评估方法[J].计算机工程与应用,2016,52(19):97-101.
[2]杨云雪,鲁骁,董军.基于企业环境的网络安全风险评估[J].计算机科学与探索,2016,10(10):1387-1397.
[3]崔力民,等.智能电网信息通信系统风险评估的模糊测度方法[J].电力建设,2017,38(5):52-61.
[4]王姣,范科峰,莫玮.基于模糊集和DS证据理论的信息安全风险评估方法[J].计算机应用研究,2017,34(11):3432-3436.
[5]赵健.物联网系统安全威胁和风险评估[J].北京邮电大学学报,2017,40(s1):135-139.
[6]杨国泰,等.电力CPS信息网络脆弱性及其评估方法[J].中国电力,2018,51(1):83-89.
[7]南开辉,等.分布式电源配电网造价风险评估仿真研究[J].计算机仿真,2017,34(3):96-99.
[8]胡康,陈亮.面向服装企业的文档知识主动共享方法.[J].西安工程大学学报,2017,4(31):544-550.
[9]陈晓燕,唐年庆.大数据环境下的Web网络风险估计[J].现代电子技术,2017,40(24):56-58.
[10]王钰楠,等.基于复杂网络理论的主动配电网多级运行风险快速评估[J].电力系统自动化,2016,40(14):65-71.
[2]杨云雪,鲁骁,董军.基于企业环境的网络安全风险评估[J].计算机科学与探索,2016,10(10):1387-1397.
[3]崔力民,等.智能电网信息通信系统风险评估的模糊测度方法[J].电力建设,2017,38(5):52-61.
[4]王姣,范科峰,莫玮.基于模糊集和DS证据理论的信息安全风险评估方法[J].计算机应用研究,2017,34(11):3432-3436.
[5]赵健.物联网系统安全威胁和风险评估[J].北京邮电大学学报,2017,40(s1):135-139.
[6]杨国泰,等.电力CPS信息网络脆弱性及其评估方法[J].中国电力,2018,51(1):83-89.
[7]南开辉,等.分布式电源配电网造价风险评估仿真研究[J].计算机仿真,2017,34(3):96-99.
[8]胡康,陈亮.面向服装企业的文档知识主动共享方法.[J].西安工程大学学报,2017,4(31):544-550.
[9]陈晓燕,唐年庆.大数据环境下的Web网络风险估计[J].现代电子技术,2017,40(24):56-58.
[10]王钰楠,等.基于复杂网络理论的主动配电网多级运行风险快速评估[J].电力系统自动化,2016,40(14):65-71.