publication . Conference object . Part of book or chapter of book . Other literature type . 2017

Bayesian Network Models in Cyber Security: A Systematic Review

Chockalingam, S.; Pieters, W.; Herdeiro Teixeira, A.M.; van Gelder, P.H.A.J.M.; Lipmaa, Helger; Mitrokotsa, Aikaterini; Matulevicius, Raimundas;
Open Access English
  • Published: 01 Jan 2017 Journal: Proceedings of the Nordic Conference on Secure IT Systems (Nordic 2017), volume 10,674 (issn: 0302-9743, Copyright policy)
  • Country: Netherlands
Abstract
Bayesian Networks (BNs) are an increasingly popular modelling technique in cyber security especially due to their capability to overcome data limitations. This is also instantiated by the growth of BN models development in cyber security. However, a comprehensive comparison and analysis of these models is missing. In this paper, we conduct a systematic review of the scientific literature and identify 17 standard BN models in cyber security. We analyse these models based on 9 different criteria and identify important patterns in the use of these models. A key outcome is that standard BNs are noticeably used for problems especially associated with malicious inside...
Persistent Identifiers
Subjects
free text keywords: Bayesian attack graph, Bayesian Network, Cyber security, Information security, Insider threat, Data limitations, Computer security, computer.software_genre, computer, Scientific literature, Computer science
Download fromView all 4 versions
NARCIS
Conference object . 2017
Provider: NARCIS
https://repository.tudelft.nl/...
Part of book or chapter of book
Provider: UnpayWall
TU Delft Repository
Conference object . 2017
Provider: NARCIS
http://dx.doi.org/10.1007/978-...
Other literature type . 2017
Provider: Datacite
54 references, page 1 of 4

1. WEF: Partnering for Cyber Resilience: Towards the Quanti cation of Cyber Threats. (2015)

2. Yu, S., Wang, G., Zhou, W.: Modeling malicious activities in cyber space. IEEE network 29, 83-87. (2015)

3. Ben-Gal, I.: Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability. John Wiley & Sons, Ltd. (2008)

4. Darwiche, A.: Bayesian networks. Foundations of Arti cial Intelligence 3. (2008) [OpenAIRE]

5. Landuyt, D., et al.: A review of Bayesian belief networks in ecosystem service modelling. Environmental Modelling & Software 46, 1-11. (2013)

6. Uusitalo, L.: Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling 203, 312-318. (2007) [OpenAIRE]

7. Nikovski, D.: Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. IEEE Transactions on Knowledge and Data Engineering 12(4), pp.509-516. (2000)

8. Nakatsu, R.T.: Reasoning with Diagrams: Decision-Making and Problem-Solving with Diagrams. John Wiley & Sons. (2009)

9. Phan, T.D., et al.: Applications of Bayesian belief networks in water resource management: A systematic review. Environmental Modelling & Software 85, 98-111. (2016)

10. Kordy, B., Pietre-Cambacedes, L., Schweitzer, P.: DAG-based attack and defense modeling: Don't miss the forest for the attack trees. Computer science review 13, 1-38. (2014)

11. Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using bayesian attack graphs. IEEE Transactions on Dependable and Secure Computing 9, 61-74. (2012)

12. Frigault, M., Wang, L.: Measuring network security using bayesian network-based attack graphs. IEEE. (2008)

13. Liu, Y., Man, H.: Network vulnerability assessment using Bayesian networks. In: Proc. SPIE, pp. 61-71. (2005)

14. Kwan, M., Chow, K.-P., Law, F., Lai, P.: Reasoning about evidence using Bayesian networks. In: IFIP International Conference on Digital Forensics, pp. 275-289. (2008)

15. Axelrad, E.T., Sticha, P.J., Brdiczka, O., Shen, J.: A Bayesian network model for predicting insider threats. In: Security and Privacy Workshops, pp. 82-89. (2013)

54 references, page 1 of 4
Abstract
Bayesian Networks (BNs) are an increasingly popular modelling technique in cyber security especially due to their capability to overcome data limitations. This is also instantiated by the growth of BN models development in cyber security. However, a comprehensive comparison and analysis of these models is missing. In this paper, we conduct a systematic review of the scientific literature and identify 17 standard BN models in cyber security. We analyse these models based on 9 different criteria and identify important patterns in the use of these models. A key outcome is that standard BNs are noticeably used for problems especially associated with malicious inside...
Persistent Identifiers
Subjects
free text keywords: Bayesian attack graph, Bayesian Network, Cyber security, Information security, Insider threat, Data limitations, Computer security, computer.software_genre, computer, Scientific literature, Computer science
Download fromView all 4 versions
NARCIS
Conference object . 2017
Provider: NARCIS
https://repository.tudelft.nl/...
Part of book or chapter of book
Provider: UnpayWall
TU Delft Repository
Conference object . 2017
Provider: NARCIS
http://dx.doi.org/10.1007/978-...
Other literature type . 2017
Provider: Datacite
54 references, page 1 of 4

1. WEF: Partnering for Cyber Resilience: Towards the Quanti cation of Cyber Threats. (2015)

2. Yu, S., Wang, G., Zhou, W.: Modeling malicious activities in cyber space. IEEE network 29, 83-87. (2015)

3. Ben-Gal, I.: Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability. John Wiley & Sons, Ltd. (2008)

4. Darwiche, A.: Bayesian networks. Foundations of Arti cial Intelligence 3. (2008) [OpenAIRE]

5. Landuyt, D., et al.: A review of Bayesian belief networks in ecosystem service modelling. Environmental Modelling & Software 46, 1-11. (2013)

6. Uusitalo, L.: Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling 203, 312-318. (2007) [OpenAIRE]

7. Nikovski, D.: Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics. IEEE Transactions on Knowledge and Data Engineering 12(4), pp.509-516. (2000)

8. Nakatsu, R.T.: Reasoning with Diagrams: Decision-Making and Problem-Solving with Diagrams. John Wiley & Sons. (2009)

9. Phan, T.D., et al.: Applications of Bayesian belief networks in water resource management: A systematic review. Environmental Modelling & Software 85, 98-111. (2016)

10. Kordy, B., Pietre-Cambacedes, L., Schweitzer, P.: DAG-based attack and defense modeling: Don't miss the forest for the attack trees. Computer science review 13, 1-38. (2014)

11. Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using bayesian attack graphs. IEEE Transactions on Dependable and Secure Computing 9, 61-74. (2012)

12. Frigault, M., Wang, L.: Measuring network security using bayesian network-based attack graphs. IEEE. (2008)

13. Liu, Y., Man, H.: Network vulnerability assessment using Bayesian networks. In: Proc. SPIE, pp. 61-71. (2005)

14. Kwan, M., Chow, K.-P., Law, F., Lai, P.: Reasoning about evidence using Bayesian networks. In: IFIP International Conference on Digital Forensics, pp. 275-289. (2008)

15. Axelrad, E.T., Sticha, P.J., Brdiczka, O., Shen, J.: A Bayesian network model for predicting insider threats. In: Security and Privacy Workshops, pp. 82-89. (2013)

54 references, page 1 of 4
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