面向拟态安全防御的异构功能等价体调度算法

doi:10.11959/j.issn.1000-436x.2018124

摘要/Abstract

摘要：

拟态安全防御的一个关键环节是异构功能等价体的调度，现有的调度策略缺乏对冗余体间相似度的考虑，且调度算法较为单一。基于此，提出了一种兼顾动态性和可靠性的异构功能等价体调度算法——随机种子最小相似度算法，首先随机确定种子冗余体，然后再根据相似度指标选择整体相似度最小的最终调度方案。理论分析和仿真实验表明，该算法的调度周期远高于最长相异性距离算法，而失效率远低于随机调度算法，在动态性和可靠性之间达到了较好的平衡。

关键词: 拟态安全防御, 异构冗余调度, 相似度, 随机种子

Abstract:

The scheduling of heterogeneous redundancies is one of the key lines of mimic security defense,but the existing scheduling strategies are lack of consideration about the similarity among redundancies and the scheduling algorithms are incomprehensive.A new scheduling algorithm called random seed & minimum similarity (RSMS) algorithm was proposed,which combined dynamics and reliability by determining a scheduling scheme with minimum global-similarity after choosing a seed-redundancy randomly.Theoretical analysis and simulation results show that RSMS algorithm possessed a far longer scheduling cycle than maximum dissimilarity algorithm,as well as a far lower failure rate than random scheduling algorithm,which represents an effective balance between dynamics and reliability.

Key words: mimic security defense, heterogeneous redundancies scheduling, similarity, random seed

中图分类号:

TP309

刘勤让,林森杰,顾泽宇. 面向拟态安全防御的异构功能等价体调度算法[J]. 通信学报, 2018, 39(7): 188-198.

Qinrang LIU,Senjie LIN,Zeyu GU. Heterogeneous redundancies scheduling algorithm for mimic security defense[J]. Journal on Communications, 2018, 39(7): 188-198.

图/表 14

图1

表1

符号表示"

符号	定义
?	异构功能等价体集合，即冗余池
N	冗余池内异构功能等价体数
P_i	异构功能等价体i，1<i<N
R	调度规则集合
F	调度方案集合
E_z	调度方案z的调度向量
$L_{m}^{i}$	组件数为m的功能等价体P_i的特征向量
Y_k	第k个组件的特征相似矩阵
h _ij	P _i、P_j之间的相似度，1≤i,j≤N,i≠j
S	调度方案的整体相似度
$h_{\lim}$	相似度阈值

表1

图2

图3

表2

4种算法下系统正常工作的概率"

算法	正常工作概率
随机调度	$P_{wR} = \frac{1}{k - 2} \sum_{r = 3}^{k} (\frac{1}{C} \sum_{f_{x}^{r} \in Ω} {P_{\geq τ}^{wrong} \|}_{f_{x}^{r}})$
MD	$P_{wL} = \frac{1}{k - 2} \sum_{r = 3}^{k} {P_{\geq τ}^{wrong} \|}_{\min ({S \|}_{f_{x}^{r}})}$
OMD	$P_{wB} = \frac{1}{k - 2} \sum_{r = 3}^{k} {P_{\geq τ}^{wrong} \|}_{\min [σ ({\forall h_{i j} \|}_{h_{i j} \in f_{x}^{r}})]}$
RSMS	$P_{wRSMS} = \frac{1}{k - 2} \sum_{r = 3}^{k} [{\frac{1}{N} \sum_{f_{x}^{r} \in Ω & P_{s} \in f_{x}^{r}} P_{\geq τ}^{wrong} \|}_{\min ({S \|}_{f_{x}^{r}})}]$

表2

图4

表3

表4

图5

表5

表6

图6

表7

图7

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算法	r =3	r =4	r =5
RSMS算法	7.31	5.08	3.49
随机调度算法	84.00	126.00	126.00
MD算法	1.00	1.00	1.00
OMD算法	1.00	1.00	1.00

算法	r =3	r =4	r =5
RSMS算法	0.155 0	0.181 5	0.204 4
随机调度算法	0.272 3	0.272 3	0.272 3
MD算法	0.114 3	0.153 2	0.195 3
OMD算法	0.218 3	0.283 0	0.267 3

算法	r= 3	r= 4	r= 5
RSMS算法	7.190 9×10^-4	2.365 6×10^-5	9.210 3×10^-5
随机调度算法	0.002 2	8.022 7×10^-5	1.813 4×10^-4
MD算法	3.675 1×10^-4	1.335 0×10^-5	6.443 7×10^-5
OMD算法	0.001 3	8.450 3×10^-5	1.677 2×10^-4

算法	r= 3	r= 4	r= 5
RSMS算法	2.766 4×10^-4	3.806 4×10^-6	2.574 5×10^-5
随机调度算法	7.664 7×10^-4	1.515 2×10^-5	3.518 6×10^-5
MD算法	1.141 9×10^-4	8.876 5×10^-7	6.782 0×10^-6
OMD算法	3.598 9×10^-4	1.058 3×10^-5	1.118 2×10^-5

性能	动态性	可靠性
高↓低	随机调度算法	MD算法
	RSMS算法	RSMS算法
	MD算法/OMD算法	随机调度算法/OMD算法