Skip to main content

A Critical Node-Centric Approach to Enhancing Network Security

  • Conference paper
  • First Online:
Dynamics of Information Systems (DIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14321))

Included in the following conference series:

Abstract

In the realm of network analysis, the identification of critical nodes takes center stage due to their pivotal role in maintaining network functionality. These nodes wield immense importance, as their potential failure has the capacity to disrupt connectivity and pose threats to network security. This paper introduces an innovative approach to assess the vulnerability of these critical nodes by assessing their significance within the network structure. Through rigorous numerical analysis, our methodology not only demonstrates its effectiveness but also offers valuable insights into network dynamics. To enhance network robustness and, consequently, enhance network security, we formulate the network as a non-linear optimization problem. Our overarching objective is to determine the optimal security level, quantified as a resource allocation cost, for these critical nodes, ultimately aligning with our network security and robustness objectives.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmed, M., Naser Mahmood, A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)

    Article  Google Scholar 

  2. Albert, R., Jeong, H., Barabási, A.L.: Diameter of the world-wide web. Nature 401(6749), 130–131 (1999)

    Article  Google Scholar 

  3. Alozie, G.U., Arulselvan, A., Akartunalı, K., Pasiliao, E.L., Jr.: A heuristic approach for the distance-based critical node detection problem in complex networks. J. Oper. Res. Soc. 73(6), 1347–1361 (2022)

    Article  Google Scholar 

  4. Amini, M.H., Arasteh, H., Siano, P.: Sustainable smart cities through the lens of complex interdependent infrastructures: panorama and state-of-the-art. In: Amini, M.H., Boroojeni, K.G., Iyengar, S.S., Pardalos, P.M., Blaabjerg, F., Madni, A.M. (eds.) Sustainable Interdependent Networks II. SSDC, vol. 186, pp. 45–68. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98923-5_3

    Chapter  Google Scholar 

  5. Aringhieri, R., Grosso, A., Hosteins, P., Scatamacchia, R.: A general evolutionary framework for different classes of critical node problems. Eng. Appl. Artif. Intell. 55, 128–145 (2016)

    Article  Google Scholar 

  6. Arulselvan, A.: Network model for disaster management. Ph.D. thesis, University of Florida Gainesville (2009)

    Google Scholar 

  7. Arulselvan, A., Commander, C., Elefteriadou, L., Pardalos, P.: Detecting critical nodes in sparse graphs. Comput. Oper. Res. 36, 2193–2200 (2009). https://doi.org/10.1016/j.cor.2008.08.016

    Article  MathSciNet  Google Scholar 

  8. Arulselvan, A., Commander, C.W., Elefteriadou, L., Pardalos, P.M.: Detecting critical nodes in sparse graphs. Comput. Oper. Res. 36(7), 2193–2200 (2009)

    Article  MathSciNet  Google Scholar 

  9. Bae, J., Kim, S.: Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Phys. A 395, 549–559 (2014)

    Article  MathSciNet  Google Scholar 

  10. Berger, A., Grigoriev, A., van der Zwaan, R.: Complexity and approximability of the k-way vertex cut. Networks 63(2), 170–178 (2014)

    Article  MathSciNet  Google Scholar 

  11. Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)

    Article  Google Scholar 

  12. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  13. Chen, D., Lü, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Phys. A 391(4), 1777–1787 (2012)

    Article  Google Scholar 

  14. Commander, C.W., Pardalos, P.M., Ryabchenko, V., Uryasev, S., Zrazhevsky, G.: The wireless network jamming problem. J. Comb. Optim. 14, 481–498 (2007)

    Article  MathSciNet  Google Scholar 

  15. Dang, F., Zhao, X., Yan, L., Wu, K., Li, S.: Research on network intrusion response method based on Bayesian attack graph, pp. 639–645 (2023)

    Google Scholar 

  16. Das, K., Samanta, S., Pal, M.: Study on centrality measures in social networks: a survey. Soc. Netw. Anal. Min. (13) (2018)

    Google Scholar 

  17. Devkota, P., Danzi, M.C., Wuchty, S.: Beyond degree and betweenness centrality: alternative topological measures to predict viral targets. PLoS ONE 13(5), e0197595 (2018)

    Article  Google Scholar 

  18. Estrada, E., Rodriguez-Velazquez, J.A.: Subgraph centrality in complex networks. Phys. Rev. E 71(5), 056103 (2005)

    Article  MathSciNet  Google Scholar 

  19. Faramondi, L., Oliva, G., Pascucci, F., Panzieri, S., Setola, R.: Critical node detection based on attacker preferences, pp. 773–778 (2016)

    Google Scholar 

  20. Faramondi, L., Oliva, G., Setola, R., Pascucci, F., Esposito Amideo, A., Scaparra, M.P.: Performance analysis of single and multi-objective approaches for the critical node detection problem, pp. 315–324 (2017)

    Google Scholar 

  21. Fernandes, J.M., Suzuki, G.M., Zhao, L., Carneiro, M.G.: Data classification via centrality measures of complex networks, pp. 1–8 (2023). https://doi.org/10.1109/IJCNN54540.2023.10192048

  22. Freeman, L.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215 (1979)

    Google Scholar 

  23. Gupta, B.B., Gaurav, A., Marín, E.C., Alhalabi, W.: Novel graph-based machine learning technique to secure smart vehicles in intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 24(8), 8483–8491 (2023). https://doi.org/10.1109/TITS.2022.3174333

    Article  Google Scholar 

  24. Hage, P., Harary, F.: Eccentricity and centrality in networks. Soc. Netw. 17(1), 57–63 (1995)

    Article  Google Scholar 

  25. Hamouda, E., Mitton, N., Pavkovic, B., Simplot-Ryl, D.: Energy-aware georouting with guaranteed delivery in wireless sensor networks with obstacles. Int. J. Wirel. Inf. Netw. 16, 142–153 (2009)

    Article  Google Scholar 

  26. Hamouda, E., Mitton, N., Simplot-Ryl, D.: Energy efficient mobile routing in actuator and sensor networks with connectivity preservation. In: Frey, H., Li, X., Ruehrup, S. (eds.) ADHOC-NOW 2011. LNCS, vol. 6811, pp. 15–28. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22450-8_2

    Chapter  Google Scholar 

  27. Hao, Y.H., Han, J.H., Lin, Y., Liu, L.: Vulnerability of complex networks under three-level-tree attacks. Phys. A 462, 674–683 (2016)

    Article  MathSciNet  Google Scholar 

  28. Imran, M., Alnuem, M.A., Fayed, M.S., Alamri, A.: Localized algorithm for segregation of critical non-critical nodes in mobile ad hoc and sensor networks. Procedia Comput. Sci. 19, 1167–1172 (2013). https://doi.org/10.1016/j.procs.2013.06.166. https://www.sciencedirect.com/science/article/pii/S1877050913007746. The 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013), the 3rd International Conference on Sustainable Energy Information Technology (SEIT-2013)

  29. Invernizzi, L., et al.: Nazca: detecting malware distribution in large-scale networks (2014)

    Google Scholar 

  30. Jain, A., Reddy, B.: Node centrality in wireless sensor networks: importance, applications and advances. In: Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013, pp. 127–131 (2013). https://doi.org/10.1109/IAdCC.2013.6514207

  31. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  Google Scholar 

  32. Kim, S.: Anatomy on malware distribution networks. IEEE Access 8, 73919–73930 (2020). https://doi.org/10.1109/ACCESS.2020.2985990

    Article  Google Scholar 

  33. Kim, S., Kim, J., Kang, B.B.: Malicious URL protection based on attackers’ habitual behavioral analysis. Comput. Secur. 77, 790–806 (2018)

    Article  Google Scholar 

  34. Kivimäki, I., Lebichot, B., Saramäki, J., Saerens, M.: Two betweenness centrality measures based on randomized shortest paths. Sci. Rep. 6(1), 1–15 (2016)

    Article  Google Scholar 

  35. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)

    Article  MathSciNet  Google Scholar 

  36. Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018)

    Article  MathSciNet  Google Scholar 

  37. Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018). https://doi.org/10.1016/j.cosrev.2018.02.002. https://www.sciencedirect.com/science/article/pii/S1574013716302416

  38. Liu, X., Hong, Z., Liu, J., Lin, Y., et al.: Computational methods for identifying the critical nodes in biological networks. Brief. Bioinform. 21, 486–497 (2020)

    Article  Google Scholar 

  39. Lozano, M., Garcia-Martinez, C., Rodriguez, F.J., Trujillo, H.M.: Optimizing network attacks by artificial bee colony. Inf. Sci. 377, 30–50 (2017)

    Article  Google Scholar 

  40. Lu, K., Fang, X., Fang, N.: PN-BBN: a petri net-based Bayesian network for anomalous behavior detection. Mathematics 10(20), 3790 (2022)

    Article  Google Scholar 

  41. Lü, L., Chen, D., Ren, X.L., Zhang, Q.M., Zhang, Y.C., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016)

    Article  MathSciNet  Google Scholar 

  42. Lü, L., Zhou, T., Zhang, Q.M., Stanley, H.E.: The h-index of a network node and its relation to degree and coreness. Nat. Commun. 7(1), 10168 (2016)

    Article  Google Scholar 

  43. Mazlumi, S.H.H., Kermani, M.A.M.: Investigating the structure of the internet of things patent network using social network analysis. IEEE Internet Things J. 9(15), 13458–13469 (2022). https://doi.org/10.1109/JIOT.2022.3142191

    Article  Google Scholar 

  44. Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018). https://doi.org/10.1016/j.cosrev.2018.02.002

    Article  MathSciNet  Google Scholar 

  45. Morone, F., Makse, H.A.: Influence maximization in complex networks through optimal percolation. Nature 524(7563), 65–68 (2015)

    Article  Google Scholar 

  46. Nie, T., Guo, Z., Zhao, K., Lu, Z.M.: Using mapping entropy to identify node centrality in complex networks. Phys. A 453, 290–297 (2016)

    Article  Google Scholar 

  47. Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)

    Article  MathSciNet  Google Scholar 

  48. Sariyüce, A.E., Kaya, K., Saule, E., Çatalyiirek, Ü.V.: Incremental algorithms for closeness centrality, pp. 487–492 (2013)

    Google Scholar 

  49. Shen, Y., Nguyen, N., Xuan, Y., Thai, M.: On the discovery of critical links and nodes for assessing network vulnerability. IEEE/ACM Trans. Netw. 21, 963–973 (2013). https://doi.org/10.1109/TNET.2012.2215882

    Article  Google Scholar 

  50. Shukla, S.: Angle based critical nodes detection (ABCND) for reliable industrial wireless sensor networks. Wireless Pers. Commun. 130(2), 757–775 (2023)

    Article  Google Scholar 

  51. Stephenson, K., Zelen, M.: Rethinking centrality: methods and examples. Soc. Netw. 11(1), 1–37 (1989)

    Article  MathSciNet  Google Scholar 

  52. Tian, G., Yang, X., Li, Y., Yang, Z., Chen, G.: Hybrid weighted communication network node importance evaluation method. Front. Phys. 11 (2023). https://doi.org/10.3389/fphy.2023.1133250. https://www.frontiersin.org/articles/10.3389/fphy.2023.1133250

  53. Ventresca, M., Aleman, D.: A derandomized approximation algorithm for the critical node detection problem. Comput. Oper. Res. 43, 261–270 (2014)

    Article  MathSciNet  Google Scholar 

  54. Wandelt, S., Lin, W., Sun, X., Zanin, M.: From random failures to targeted attacks in network dismantling. Reliab. Eng. Syst. Saf. 218, 108146 (2021). https://doi.org/10.1016/j.ress.2021.108146

    Article  Google Scholar 

  55. Wang, B., Jia, J., Zhang, L., Gong, N.Z.: Structure-based Sybil detection in social networks via local rule-based propagation. IEEE Trans. Netw. Sci. Eng. 6, 523–537 (2018)

    Article  MathSciNet  Google Scholar 

  56. Yan, G., Chen, G., Eidenbenz, S.J., Li, N.: Malware propagation in online social networks: nature, dynamics, and defense implications (2011)

    Google Scholar 

  57. Yen, C.C., Yeh, M.Y., Chen, M.S.: An efficient approach to updating closeness centrality and average path length in dynamic networks, pp. 867–876 (2013). https://doi.org/10.1109/ICDM.2013.135

  58. Yi-Run, R., Song-Yang, L., Yan-Dong, X., Jun-De, W., Liang, B.: Identifying influence of nodes in complex networks with coreness centrality: decreasing the impact of densely local connection. Chin. Phys. Lett. 33(2), 028901 (2016)

    Article  Google Scholar 

  59. Zhang, S., Yu, H., et al.: Modeling and simulation of tennis serve image path correction optimization based on deep learning. Wirel. Commun. Mob. Comput. 2022 (2022)

    Google Scholar 

  60. Zheng, H., et al.: Smoke screener or straight shooter: detecting elite Sybil attacks in user-review social networks. arXiv:abs/1709.06916 (2017)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Essia Hamouda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hamouda, E. (2024). A Critical Node-Centric Approach to Enhancing Network Security. In: Moosaei, H., Hladík, M., Pardalos, P.M. (eds) Dynamics of Information Systems. DIS 2023. Lecture Notes in Computer Science, vol 14321. Springer, Cham. https://doi.org/10.1007/978-3-031-50320-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50320-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50319-1

  • Online ISBN: 978-3-031-50320-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics