Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 62-69.doi: 10.11896/jsjkx.210800107

• Novel Distributed Computing Technology and System • Previous Articles     Next Articles

Dynamic Network Security Analysis Based on Bayesian Attack Graphs

LI Jia-rui1, LING Xiao-bo2, LI Chen-xi1, LI Zi-mu1, YANG Jia-hai1, ZHANG Lei2, WU Cheng-nan2   

  1. 1 Institute for Network Sciences and Cyberspace,Tsinghua University,Beijing 100084,China
    2 State Grid Shanghai Municipal Electric Power Company,Shanghai 200122,China
    3 State Grid Shanghai Electric Power Research Institute,Shanghai 200437,China
    4 Songjiang Power Supply Company of State Grid Shanghai Municipal Electric Power Company,Shanghai 201699,China
  • Received:2021-08-11 Revised:2021-10-12 Online:2022-03-15 Published:2022-03-15
  • About author:LI Jia-rui,born in 1997,postgraduate.Her main research interests include network measurement,cybersecurity and next generation Internet.
    YANG Jia-hai,born in 1966,professor,Ph.D supervisor,is a senior member of China Computer Federation and IEEE.His main research interests include network management,internet measurement and security,cybersecurity and cyberspace mapping,cloud computing and network functions vir-tualization.
  • Supported by:
    Research on Cyberspace Vulnerability Analysis and Threat Detection in Power Monitoring System(5108-202117055A-0-0-00).

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Abstract: In order to overcome the difficulties that current attack graph model cannot reflect real-time network attack events,a method is proposed including a forward risk probability update algorithm and a forward-backward combined risk probability update algorithm,which meets the needs of real-time analyzing network security.It first performs specific quantitative analysis on the uncertainty of each node in the graph,and uses Bayesian networks to calculate their static probabilities.After that,it updates the dynamic probability of each node along the forward and backward paths according to the real-time network security events,instantly reflecting the changes of external conditions and assessing real-time risk levels across the network.Experimental results show that the method can calibrate and adjust the risk probability of each node according to the actual situation,which helps the network operator correctly understand the dangerous levels of the network and make better decision for defense and prevention of the next attack.

Key words: Attack graph, Bayesian network, Dynamic probability, Real time, Risk probability, Static probability

CLC Number: 

  • TP393.08
[1]WANG L,ISLAM T,LONG T,et al.An attack graph-basedprobabilistic security metric[C]//22nd Annual IFIP WG 11.3 Working Conference on Data and Applications Security.London:IFIP,2008:283-296.
[2]ZHANG J,WANG J D,ZHANG H W,et al.Network risk ana-lysis method based on node game vulnerability attack graph[J].Computer Science,2014,9(41):169-173.
[3]LIAO J,LI D W.A Bayesian Network inference algorithm based on message propagation and its application[J].Computer and Digital Engineering,2017,44(1):16-20,57.
[4]MUÑOZ-GONZÁLEZ L,SGANDURRA D,PAUDICE A,et al.Efficient attack graph analysis through approximate inference[J].arXiv:1606.07025,2016.
[5]Common Vulnerability Scoring System (CVSS)[EB/OL].[2019-05-30].http://www.first.org/cvss/.
[6]XIE P,LI J,OU X M,et al.Using Bayesian networks for cybersecurity analysis[C]//DSN 2010:2010 IEEE/IFIP International Conference on Dependable Systems & Networks.Chicago:IEEE,2010:211-220.
[7]POOLSAPPASIT N,DEWRI R,RAY I.Dynamic security risk management using Bayesian attack graphs[J].IEEE Transactions on Dependable and Secure Computing,2012,9(1):61-74.
[8]ASVIJA B,ESWARI R,BIJOY M B.Bayesian attack graphs for platform virtualized infrastructures in clouds[J/OL].Journal of Information Security and Applications.https://www.science-direct.com/science/article/abs/pii/S2214212619305332.
[9]WANG H,CHEN Z,ZHAO J,et al.A vulnerability assessment method in industrial internet of things based on attack graph and maximum flow[J].IEEE Access,2018,6:8599-8609.
[10]ZIMBA A,CHEN H,WANG Z.Bayesian network based weighted APT attack paths modeling in cloud computing[J].Future Generation Computer Systems,2019,96:525-537.
[11]GONZALEZ L,LUPU E.Bayesian attack graphs for securityrisk assessment[C]//IST-153 NATO Workshop on Cyber Resilience.Munich:IST-153,2017.
[12]ZHENG Y K,LV K,HU C Z.A quantitative method for evaluating network security based on attack graph[C]//Proceedings of Network and System Security.Switzerland:Springer,2017:349-358.
[13]FRIGAULT M,WANG L,SINGHAL A,et al.Measuring network security using dynamic bayesian network[C]//Procee-dings of the 4th ACMworkshop on Quality of Protection.2008:23-30.
[14]LUIS G,DANIELE S,MARTIN B,et al.Exact inference techniques for the analysis of Bayesian attack Graphs[J].IEEE Transactions on Dependable and Secure Computing,2019,16(2):231-244.
[15]HU Z,ZHU M,LIU P.Online algorithms for adaptive cyber Defenseon Bayesian attack graphs[C]//2017 Workshop on Moving Target Defense.Dallas:ACM,2017:99-109.
[16]WILLIAM S.Cryptography and Network Security:Principlesand Practice Second[OL].https://www.informit.com/store/cryptography-and-network-security-principles-and-practice-9780134444666.
[17]OU X,GOVINDAVAJHALA S,APPEL A W.MulVAL:ALogic-based Network Security Analyzer[C]//USENIX Security Symposium.2005:113-128.
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