领域独立智能规划技术及其面向自动化渗透测试的攻击路径发现研究进展

臧艺超; 周天阳; 朱俊虎; 王清贤

doi:10.11999/JEIT191056

领域独立智能规划技术及其面向自动化渗透测试的攻击路径发现研究进展

doi: 10.11999/JEIT191056

臧艺超¹,
周天阳^1, ,,
朱俊虎^{1, 2},
王清贤¹

1.
数学工程与先进计算国家重点实验室(信息工程大学) 郑州 450001
2.
国家数字交换系统工程技术研究中心郑州 450001

基金项目: 国家自然科学基金(61502528)

详细信息

作者简介:
臧艺超：男，1991年生，博士生，研究方向为路径规划，强化学习，效果评估

周天阳：男，1979年生，副教授，研究方向为网络安全，强化学习，效果评估

朱俊虎：男，1971年生，教授，研究方向为网络安全，网络模拟与效果评估

王清贤：男，1960年生，教授，研究方向为网络安全，计算复杂度，网络模拟与效果评估

通讯作者:
周天阳　aipteamzhouty@aliyun.com

中图分类号: TN915.08; TP309
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-31
- 修回日期: 2020-03-17
- 网络出版日期: 2020-07-21
- 刊出日期: 2020-09-27

Domain-Independent Intelligent Planning Technology and Its Application to Automated Penetration Testing Oriented Attack Path Discovery

1.
State Key Laboratory of Mathematical Engineering and Advanced Computing, Information & Engineering University, Zhengzhou 450001, China
2.
National Engineering Technology Research Center of the National Digital Switching System, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61502528)

摘要

摘要: 攻击路径发现是自动化渗透测试领域的重要研究方向。该文综合论述了领域独立智能规划技术在面向自动化渗透测试的攻击路径发现上的研究进展及应用前景。首先介绍了攻击路径发现的基本概念并按照技术原理将其划分为基于领域相关和领域独立规划技术的攻击路径发现方法。然后介绍了领域独立智能规划算法，包括确定性规划算法、非确定性规划算法和博弈规划的技术原理和发展状况并就各类方法在攻击路径发现中的应用进行了综述。接着分析总结了渗透测试过程的特点，对比了领域独立智能规划算法应用在面向自动化渗透测试的攻击路径发现时的优缺点。最后对攻击路径发现将来的发展方向进行了总结和展望，希望对未来进一步的研究工作有一定的参考价值。
- 领域独立智能规划技术 /
- 自动化渗透测试 /
- 攻击路径发现
Abstract: Attack path discovery is an important research direction in automated penetration testing area. This paper introduces the research progress of domain independent intelligent planning technology and its application to the field of automated penetration testing oriented attack paths discovery. Firstly, the basic concept of attack path discovery is introduced and the related algorithms are divided into domain-specific and domain-independent intelligent planning based attack path discovery algorithms separately. Secondly, the domain-independent planning algorithms are classified into deterministic planning, uncertain planning and game planning, where each of which is described from principle, development and application aspect in detail. Thirdly, this paper summarizes the characteristics of automated penetration testing and compares the advantages and disadvantages of domain independent intelligent planning algorithms adopted in automated penetration testing. Lastly, the development of automated penetration testing oriented attack path discovery is prospected. It is hoped that this paper could contribute future research works on attack path discovery.
- Domain independent intelligent planning technology /
- Automated penetration testing /
- Attack path discovery

HTML全文

表 1 领域独立智能规划算法进行攻击路径发现时的适用性总结

	类型	文献	O	U	D	R	M	优点	缺点
确定性攻击路径发现	规划图	[18]	√	×	×	×	√	能够显示描述所有可能攻击路径，可解释性强	时间复杂度高，为O(mn^k)，m为状态空间大小，n为动作空间大小，k为层数
	规划图	[20]	√	×	×	×	√	基于规划图构建启发函数，提高攻击路径发现效率	时间复杂度高，为O(mⁿ)，不适用于大规模场景m为状态空间大小，n为动作空间大小
	偏序规划	[22]	√	×	×	×	×	能够发现所有动作对之间的约束关系	需要遍历动作空间，构建约束集合，造成额外时间开销
	偏序规划	[24]	√	×	×	×	√	构造启发函数选择动作，并利用约束关系缩减规模，提高路径搜索效率
	分层任务网络	[30]	√	×	×	×	×	可解释性更强	需要专家制定分解方法
	分层任务网络	[31]	√	×	×	×	√	利用标准优化算法提高路径发现效率	需要专家制定分解方法
非确定性攻击路径发现	Determinizing	[36]	√	√	×	×	×	可扩展性好，适用多种非确定性场景	无法进行重规划
	概率优化	[41]	√	√	×	×	×	能够根据实际执行结果进行重规划	需要删除非确定性信息进行规划，无法利用规划反馈信息
	概率优化	[44]	√	×	×	×	√	构造规划图启发函数，求解效率高	构建多个规划图，造成大量冗余
	马尔可夫决策过程	[52]	√	√	×	×	√	能存储大规模网络空间状态策略，策略求解效率更高	容易陷入局部极小值
	马尔可夫决策过程	[53]	√	√	×	×	√	基于数据确定模型的参数个数和函数形式，无需人工设定，灵活方便	在较大数据集的情况下训练时间较长
	部分观测的马尔可夫决策过程	[55]	√	√	×	×	√	精确求解算法，是后续近似求解算法的基础	求解复杂度极高，当状态空间较大时无法进行规划求解
		[57]	√	√	×	×	√	首个基于点迭代的近似求解方法，求解效率相对于精确求解效率高	仅能对单主机进行规划，时间复杂度O(\|N\|\|A\|(\|S\|\|B\|+\|O\|))，其中S为状态集合，A为动作集合，O为观测状态集合，B为信念状态点集合，N为上限点集合
		[58]	√	√	×	×	√	采用前向搜索策略，采样效率更高，适合短序列场景	仅能对单主机进行规划，时间复杂度O(\|N\|(\|S\|²+\|A\|+\|O\|))其中S为状态集合，A为动作集合，O为观测状态集合，N为上限点集合
		[59]	√	√	×	×	√	采样效率高	仅能对单主机进行规划，无法扩展到网络层面，时间复杂度为O(\|S\|³\|A\|\|O\|\|B\|\|N\|)其中S为状态集合，A为动作集合，O为观测状态集合，B为信念状态点集合，N为上限点集合
		[60]	√	√	×	×	√	能够实现网络层面攻击路径发现	假定网络拓扑结构及策略稳定不变
博弈攻击路径发现	静态博弈模型	[62]	√	×	×	×	√	首次将博弈模型引入到攻防对抗	要求完全信息且攻防双方为完全理性，并且要求攻防对抗策略保持不变
	静态博弈模型	[63]	√	×	×	×	√	求解效率高	要求完全信息且攻防双方为完全理性，并且要求攻防对抗策略保持不变
	动态博弈模型	[67]	√	×	√	×	√	多轮次博弈条件下的攻击路径发现	要求完全信息且攻防双方为完全理性
		[68]	√	×	√	×	√	摒弃了完全理性和完全信息假设	复杂度较高，为O((m+n)²)，m和n分别为攻防策略集合大小
		[71]	√	×	√	×	√	摒弃了攻防双方对等信息的假设	模型复杂，求解难，现实应用场景受限
注：O：状态空间完备性；U：行为不确定性；D:过程动态性；R：资源约束性；M：路径最优性。

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