计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 516-522.doi: 10.11896/jsjkx.210500072

• 信息安全 • 上一篇    下一篇

基于双向蚁群算法的网络攻击路径发现方法

高文龙, 周天阳, 朱俊虎, 赵子恒   

  1. 战略支援部队信息工程大学 郑州 450001
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 周天阳(aipteamzhouty@aliyun.com)
  • 作者简介:(2396833257@qq.com)

Network Attack Path Discovery Method Based on Bidirectional Ant Colony Algorithm

GAO Wen-long, ZHOU Tian-yang, ZHU Jun-hu, ZHAO Zi-heng   

  1. Information Engineering University,Zhengzhou 450001,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:GAO Wen-long,born in 1997,postgra-duate.His main research interests include cyber security and intelligent planning.
    ZHOU Tian-yang,born in 1979,asso-ciate professor.His main research inte-rests include software vulnerability ana-lysis,virtualization-based security technology and application and penetration test.

摘要: 在渗透测试领域,进行攻击路径发现对实现攻击自动化具有重要意义。现有的攻击路径发现算法大多适用于静态全局环境,且存在因状态空间爆炸导致求解失败的问题。为解决动态网络环境下的攻击路径发现问题,提高路径发现效率,提出了基于双向蚁群算法的网络攻击路径发现方法(Attack Path Discovery-Bidirectional Ant Colony Algorithm,APD-BACO)。首先,对网络信息进行建模表示,定义攻击代价;然后,提出一种新的双向蚁群算法进行攻击路径发现,主要的改进包括不同的搜索策略、交叉优化操作和新的信息素更新方式等,仿真实验验证了改进的质量和效率,同时与其他路径发现方法进行对比,结果表明所提方法在较大网络规模下具有一定的时间或空间优势。在攻击路径主机发生故障时,采用重规划机制实现局部区域的攻击路径发现,更适合实际自动化渗透测试下的攻击路径发现。

关键词: 动态环境, 攻击路径发现, 双向蚁群算法, 重规划, 自动化渗透测试

Abstract: In the field of penetration testing,the discovery of attack paths is of great significance to the realization of attack automation.Most of the existing attack path discovery algorithms are suitable for static global environments,and there is a problem that the solution fails due to the explosion of the state space.To solve the problem of attack path discovery under dynamic network environment and improve the efficiency of path discovery,a method of network attack path discovery based on bidirectional ant co-lony algorithm is proposed.First,model the network information and define the attack cost.Then,a new two-way ant colony algorithm is proposed for attack path discovery.The main improvements include different search strategies,cross-optimization operations and new pheromone update methods,etc.Simulation experiments verify the improved quality and efficiency.At the same time,compared with other path discovery methods,it has a certain time or space advantages in large network scale.When the attack path host fails,the re-planning mechanism is used to realize the attack path discovery in the local area,which is more suitable for attack path discovery under actual automated penetration testing.

Key words: Attack path discovery, Automated penetration testing, Bidirectional ant colony algorithm, Dynamic environment, Re-planning

中图分类号: 

  • TP393
[1] STEFINKO Y,PISKOZUB A,BANAKH R.Manual and automated penetration testing.Benefits and drawbacks.Modern tendency[C]//2016 13th International Conference on Modern Problems of Radio Engineering.Telecommunications and Computer Science(TCSET).IEEE,2016:488-491.
[2] ALMUBAIRIK N A,WILLS G.Automated penetration testingbased on a threat model[C]//Internet Technology & Secured Transactions.IEEE,2017:413-414.
[3] LUAN J,JIAN W,XUE M.Automated Vulnerability Modeling and Verification for Penetration Testing Using Petri Nets[C]//International Conference on Cloud Computing & Security.Cham:Springer,2016:71-82.
[4] KUCAN B.Automated Penetration Testing with CORE IM-PACT 4.0[J/OL].https://www.helpnetsecurity.com/2004/05/24/automated-penetration-testing-with-core-impact-40/.
[5] OU X,BOYER W F,MCQUEEN M A.A scalable approach to attack graph generation[C]//Proceedings of the 13th ACM Conference on Computer and Communications Security.2006:336-345.
[6] CASTILLO L A,FERNÁNDEZ-OLIVARES J,GARCIA-PEREZ O,et al.Efficiently Handling Temporal Knowledge in an HTN Planner[C]//ICAPS.2006:63-72.
[7] RICHTER S,WESTPHAL M.The LAMA planner:Guidingcost-based anytime planning with landmarks[J].Journal of Artificial Intelligence Research,2010,39:127-177.
[8] SUN Y,DONG W,CHEN Y.An improved routing algorithmbased on ant colony optimization in wireless sensor networks[J].IEEE Communications Letters,2017,21(6):1317-1320.
[9] ZHENG B L,LI Y H.Study on SDN Network Load Balancing Based on IACO[J].Computer Science,2019,46(6A):291-294.
[10] CAO J.Robot Global Path Planning Based on an Improved Ant Colony Algorithm[J].Journal of Computer & Communications,2016,4(2):11-19.
[11] DENG W,XU J,ZHAO H.An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem[J].IEEE Access,2019,7:20281-20292.
[12] HU X,LUO P,ZHANG X,et al.Improved ant colony optimization for weapon-target assignment[J].Mathematical Problems in Engineering,2018:1-14.
[13] YUE L,CHEN H.Unmanned vehicle path planning using a novel ant colony algorithm[J].EURASIP Journal on Wireless Communications and Networking,2019,2019(1):1-9.
[14] XUAN C,DAN L.Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm[J].Cluster Computing,2017(4):1-9.
[15] LUAN J,YAO Z,ZHAO F,et al.A novel method to solve supplier selection problem:Hybrid algorithm of genetic algorithm and ant colony optimization[J].Mathematics and Computers in Simulation,2019,156:294-309.
[16] CHEN G,LIU J.Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method[J].Computational Intelligence and Neuroscience,2019,2019(b):1-10.
[17] DAI X,LONG S,ZHANG Z,et al.Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method[J].Frontiers in Neurorobotics,2019,13:15-23.
[18] NECULA R,BREABAN M,RASCHIP M.Tackling DynamicVehicle Routing Problem with Time Windows by means of Ant Colony System[C]//Evolutionary Computation.IEEE,2017:2480-2487.
[19] YEN C T,CHENG M F.A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance[J].Microsystem Technologies,2018,24(1):125-135.
[20] COLORNI A,DORIGO M,MANIEZZO V.An Investigation of some Properties of an Ant Algorithm[C]//Parallel Problem Solving from Nature 2,PPSN-II.Brussels,Belgium,Elsevier,1992:28-30.
[21] WHITLEY D.A genetic algorithm tutorial[J].Statistics andComputing,1994,4(2):65-85.
[22] SUI L L,CHEN X.Improved Algorithm of Robot Online Path Planning[J].Chinese Journal of Computer Engineering and Applications,2010,46(28):36-39.
[23] CHEN R N,WEN C C,PENG L,et al.Application of Improved A* Algorithm in Robot Indoor Path Planning[J].Chinese Journal of Computer Applications,2019,39(4):1006-1011.
[24] OBES J L,SARRAUTE C,RICHARTE G.Attack Planning in the Real World[J].arXiv:1306.4044,2013.
[25] HOFFMANN J.The Metric-FF Planning System:TranslatingIgnoring Delete Lists to Numeric State Variables[J].Journal of Artificial Intelligence Research,2011,20:291-341.
[1] 罗熊丰, 翟象平.
基于空间运动约束的无人机碰撞回避规划
Collision Avoidance Planning for Unmanned Aerial Vehicles Based on Spatial Motion Constraints
计算机科学, 2022, 49(9): 194-201. https://doi.org/10.11896/jsjkx.210700107
[2] 刘敏,曾文华,刘玉珍.
动态进化多目标优化中的串式记忆方法
Bunchy Memory Method for Dynamic Evolutionary Multi-objective Optimization
计算机科学, 2016, 43(12): 241-247. https://doi.org/10.11896/j.issn.1002-137X.2016.12.044
[3] 李娟妮,华庆一,姬翔.
移动环境中任务分析及任务建模方法
Task Analysis and Task Modeling Method in Mobile Environment
计算机科学, 2014, 41(10): 210-215. https://doi.org/10.11896/j.issn.1002-137X.2014.10.045
[4] 肖国宝,严宣辉.
一种新型协作多机器人路径规划算法
New Cooperative Multi-robot Path Planning Algorithm
计算机科学, 2013, 40(4): 217-220.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!