Multi-State Causal Representation and Inference Model in Uncertain Network Attack Scenarios

doi:10.3969/j.issn.1671-1122.2025.05.010

Abstract

Abstract:

One of the challenges in the field of cybersecurity is to conduct a systematic analysis of the uncertainties of cyber-attacks. To solve this challenge, attack graphs are widely used in network security, aiming to describe attacker behavior characteristics and construct attack scenarios. However, current attack graph tools, such as attribute attack graphs, state attack graphs, and Bayesian attack graphs, cannot comprehensively consider the uncertainty factors in network attacks and provide a unified framework for describing network uncertainties. In addition, the time complexity of the algorithm related to calculating the risk probability of nodes in the current attack graph is relatively high, which is difficult to apply in practice. To solve the above problems, this paper proposed a multi-state Dynamic Uncertain Causality Attack Graph (M-DUCAG) model and a node risk probabilistic inference algorithm based on one-side causal chains (One Side-CCRP) to represent and inference the uncertainty factors of the network. The M-DUCAG could represent multiple states of nodes and describe the uncertainties in the process of network attacks based on alarm information. The One Side-CCRP algorithm effectively improved the efficiency and accuracy of inference by expanding the upstream causal chains of the node. Experiments show that the M-DUCAG model is robust in dealing with parameter disturbances and can effectively represent the uncertainties in the process of network attacks. Compared with the variable elimination method, the One Side-CCRP algorithm has higher inference efficiency under limited number alarm evidence, which can satisfy the needs of real-world inference applications.

Key words: dynamic uncertain causality attack graph, probability attack graph, uncertainty factors, vulnerability

CLC Number:

TP309

DONG Chunling, FENG Yu, FAN Yongkai. Multi-State Causal Representation and Inference Model in Uncertain Network Attack Scenarios[J]. Netinfo Security, 2025, 25(5): 778-793.

Figures/Tables 18

References 29

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指标	等级
AV	L：0.359；A：0.646；N：1.0
AC	H：0.35；M：0.61；L：0.71
Au	M：0.45；S：0.56；N：0.704

网络安全等级	A_S_;_V概率取值
薄弱	1
一般	0.9
严格	0.8

节点	D-CCRP因果链	Pr (X_v)	O-CCRP因果链	Pr (X_v)
S_1,1	—	—	—	—
S_1,2	(A_S1,2;V1,1+A_V3;S1,2·A_S3,1;V+ A_V4;S1,2·A_S4,1;V4)/3	0.73	A_S_1,2;_V_1,1	0.5
V_3,0	(A_S1,2;V1,1·A_V3,0;S1,2+A_S3,1;V3,0)/2	0.5	A_S_1,2;_V_1,1	0.1
V_3,1	(A_S1,2;V1,1·A_V3,1;S1,2+A_S3,1;V3,1)/2	0.65	A_S_1,2;_V_1,1·A_V_3,1;_S_1,2	0.4
V_4,0	(A_S1,2;V1,1·A_V4,0;S1,2+A_S4,1;V4,0)/2	0.6	A_S_1,2;_V_1,1·A_V_4,0;_S_1,2	0.4
V_4,1	(A_S1,2;V1,1·A_V4,1;S1,2+A_S4,1;V4,1)/2	0.45	A_S_1,2;_V_1,1·A_V_4,1;_S_1,2	0.1

ID	描述信息	IP地址	攻击行为节点	漏洞CVE编号	攻击发生概率
S₁	HOST1	192.168.9.35	V₃, V₁₀	CVE-2013-3940	0.86
			V₃, V₁₀	CVE-2012-0002	0.86
			V₁₃	零日漏洞攻击	0.80
S₂	HOST2	192.168.9.36	D₁	Inside Attack	1.00
S₃	SQL Server	192.168.10.72	V₄, V₆, V₇, V₁₂	CVE-2016-7253	0.80
S₄	FTP Server	192.168.10.73	V₈, V₁₁	CVE-2015-4108	0.86
S₄	FTP Server	192.168.10.73	V₈, V₁₁	CVE-2019-10009	0.80
S₅	Workstation	192.168.10.105	V₉	CVE-2019-5541	0.80
S₅	Workstation	192.168.10.105	V₉	CVE-2019-5524	0.80
S₆	Mail Server	192.168.11.15	V₂	CVE-2020-14066	0.80
S₆	Mail Server	192.168.11.15	V₅	零日漏洞攻击	0.80
S₇	Web Server	192.168.11.16	V₁	CVE-2017-12728	0.25

攻击序列	模拟攻击序列	攻击候选序列及其概率
1	B₁→V₁→S₇→ V₃→S₁→V₇→S₃	B₁(BX₁)→V_1,0→S_7,2→V_3,1→S_1,2→V_7,0→ S_3,2(0.2983) B₁(BX₁)→V_1,0→S_7,2→V_4,0→S_3,2(0.2983) BX₁→V_2,0→S_6,2→V_6,0→S_3,2(0.3055)
2	D₁→S₂→V₁₃→ S₁→V₈→S₄	D₁→S_2,2→V_13,0→S_1,2→V₈→S_4,2(0.4590) D₁→S_2,2→V_13,0→S_1,2→V_9,1→S_5,2→V₁₁→ S_4,2(0.1500) D₁→S_2,2→V_10,1→S_1,2→V₈→S_4,2(0.2870)
3	1）B₂→V₅→ S₆→V₆→S₃ 2）D₁→S₂→V₁₀→ S₁→V₈→S₄	B₂(BX₁)→V_2,0→S_6,2→V_6,0→S_3,2(0.3732) B₂(BX₁)→V_5,0→S_6,2→V_6,0→S_3,2(0.3732) D₁→S_2,2→V_10,1→S_1,2→V_8,0→S_4,2(0.3420) D₁→S_2,2→V_10,1→S_1,2→V_8,1→S_4,2(0.3690)