Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 516-522.doi: 10.11896/jsjkx.210500072

• Information Security • Previous Articles     Next Articles

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.

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

CLC Number: 

  • TP393
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