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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 10 Sayı: 3

Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks

Mücahit Sülü(INONU UNIVERSITY)0000-0002-4114-1390
Resul Daş(FIRAT UNIVERSITY)0000-0002-6113-4649
Pages
300–306
Publication Date
2022-07-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therefore, research on dynamic graph solutions and visualization in the visual analysis of cyberattacks is presented.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Threat intelligence enables us to make faster, more informed, data-backed security decisions and change their behavior from reactive to proactive in the fight against threat actors.
📊
Key Findings & Data:In this study, graph visualization is discussed for the intelligible and accurate analysis of complex cyber threat intelligence data, including network attacks.
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Conclusion & Impact:Therefore, research on dynamic graph solutions and visualization in the visual analysis of cyberattacks is presented.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:cyber security · graph visualization · dynamic graph

Abstract

Threat intelligence enables us to make faster, more informed, data-backed security decisions and change their behavior from reactive to proactive in the fight against threat actors. Cyber threat intelligence sources include open-source intelligence, social media intelligence, human intelligence, technical intelligence, device log files, forensically acquired data or intelligence from the internet traffic, and data derived for the deep and dark web. In this study, graph visualization is discussed for the intelligible and accurate analysis of complex cyber threat intelligence data, including network attacks. The processes of collecting, cleaning, organizing, and visualizing cyber intelligence data in different formats and contents on a single platform are given step by step. Dynamic graphs play an active role in these systems, where the attack locations and targets from different points are constantly variable. Therefore, research on dynamic graph solutions and visualization in the visual analysis of cyberattacks is presented.

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Cite This Article

Sülü, M., Daş, R. (2022). Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks. Balkan Journal of Electrical and Computer Engineering, Cilt: 10 Sayı: 3, 300–306. https://doi.org/10.17694/bajece.1090145

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References (24)

  1. Y. Bu ̈rhan and R. Das ̧, “Co-author link prediction from academic databases,” Gazi University, Journal of Polytechnic, vol. 20, no. 4, pp. 787–800, Dec. 2017. [Online]. Available: http://dergipark.gov.tr/ download/article- file/387477
  2. L. Yang, E. Cheng, and Z. M. O ̈zsoyog ̆lu, “Efficient path-based computations on pedigree graphs with compact encodings,” BMC Bioinformatics, vol. 13, no. S3, p. S14, Dec. 2012. [Online]. Available: https://bmcbioinformatics.biomedcentral. com/articles/10.1186/1471- 2105- 13- S3- S14
  3. Q. Guo, X. Qiu, X. Xue, and Z. Zhang, “Syntax-guided text generation via graph neural network,” Science China Information Sciences, vol. 64, no. 5, p. 152102, May 2021. [Online]. Available: http://link.springer.com/10.1007/s11432-019-2740-1
  4. B. Xie, C. Qi, H. Ben, and W. Yu, “The applications of graph theory in electric network,” in 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). Beijing, China: IEEE, Aug. 2019, pp. 780–784. [Online]. Available: https: //ieeexplore.ieee.org/document/9168962/
  5. D. P. Sinha, “A pairing between graphs and trees,” arXiv:math/0502547, Oct. 2006, arXiv: math/0502547. [Online]. Available: http://arxiv.org/ abs/math/0502547
  6. S. A. M. A. Junid, N. M. Tahir, Z. A. Majid, and M. F. M. Idros, “Potential of graph theory algorithm approach for DNA sequence alignment and comparison,” in 2012 Third International Conference on Intelligent Systems Modelling and Simulation. Kota Kinabalu, Malaysia: IEEE, Feb. 2012, pp. 187–190. [Online]. Available: http://ieeexplore.ieee.org/document/6169697/
  7. J. Zhao, Q. Yan, J. Li, M. Shao, Z. He, and B. Li, “TIMiner: Automatically extracting and analyzing categorized cyber threat intelligence from social data,” Computers & Security, vol. 95, p. 101867, Aug. 2020. [Online]. Available: https://linkinghub.elsevier.com/ retrieve/pii/S0167404820301395
  8. OASIS, “STIXTM version 2.0. part 3: Cyber observable core concepts.”
  9. E. Rossi, B. Chamberlain, F. Frasca, D. Eynard, F. Monti, and M. Bronstein, “Temporal graph networks for deep learning on dynamic graphs,” arXiv:2006.10637 [cs, stat], Oct. 2020, arXiv: 2006.10637. [Online]. Available: http://arxiv.org/abs/2006.10637
  10. F. Harary and G. Gupta, “Dynamic graph models,” Mathematical and Computer Modelling, vol. 25, no. 7, pp. 79–87, Apr. 1997. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/ S0895717797000502
  11. M. Krzywinski, I. Birol, S. J. Jones, and M. A. Marra, “Hive plots– rational approach to visualizing networks,” Briefings in Bioinformatics, vol. 13, no. 5, pp. 627–644, Sep. 2012. [Online]. Available: https://academic.oup.com/bib/article- lookup/doi/10.1093/bib/bbr069
  12. R. Das and I. Turkoglu, “Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method,” Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 6635–6644, Apr. 2009. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417408005952
  13. S. Noel, E. Harley, K. Tam, M. Limiero, and M. Share, “Chapter 4 - cygraph: Graph-based analytics and visualization for cybersecurity,” in Cognitive Computing: Theory and Applications, ser. Handbook of Statistics, V. N. Gudivada, V. V. Raghavan, V. Govindaraju, and C. Rao, Eds. Elsevier, 2016, vol. 35, pp. 117–167. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169716116300426
  14. “Global international migration flows | Wittgenstein Centre.” [Online]. Available: http://download.gsb.bund.de/BIB/global flow/
  15. G. Chen, “Information fusion and visualization of cyber-attack graphs,” SPIE Newsroom, 2007. [Online]. Available: http://www.spie.org/x14562. xml
  16. M. Alshammari and A. Rezgui, “An all pairs shortest path algorithm for dynamic graphs,” International Journal of Mathematics and Computer Science, p. 20, 2020.
  17. J. R. Nascimento, U. S. Souza, and J. L. Szwarcfiter, “Partitioning a graph into complementary subgraphs,” Graphs and Combinatorics, vol. 37, no. 4, pp. 1311–1331, Jul. 2021. [Online]. Available: https://link.springer.com/10.1007/s00373-021-02319-4
  18. S. V. Patil and D. B. Kulkarni, “K-way spectral graph partitioning for load balancing in parallel computing,” Bharati Vidyapeeth’s Institute of Computer Applications and Management, Aug. 2021. [Online]. Available: https://link.springer.com/10.1007/s41870-021-00777-w
  19. M.Baykara,R.Das ̧,andG.Tuna,“Websunucueris ̧imku ̈tu ̈klerinden web ataklarının tespitine yo ̈nelik web tabanlı log analiz platformu,” Fırat U ̈niversitesi Mu ̈hendislik Bilimleri Dergisi, vol. 28, pp. 291 – 302, 2016.
  20. “Visualized SDO relationships,” Sep. 2021. [Online]. Available: https://oasis- open.github.io/cti- documentation/examples/ visualized- sdo- relationships
  21. S. Majeed, M. Uzair, U. Qamar, and A. Farooq, “Social Network Analysis Visualization Tools: A Comparative Review,” in 2020 IEEE 23rd International Multitopic Conference (INMIC). Bahawalpur, Pakistan: IEEE, Nov. 2020, pp. 1–6. [Online]. Available: https: //ieeexplore.ieee.org/document/9318162/
  22. S. Hussain, L. Muhammad, and A. Yakubu, “Mining social media and DBpedia data using Gephi and R,” Journal of Applied Computer Science & Mathematics, vol. 12, no. 1, pp. 14–20, 2018. [Online]. Available: http://www.jacsm.ro/view/?pid=25 2
  23. G. Drakopoulos, A. Baroutiadi, and V. Megalooikonomou, “Higher order graph centrality measures for Neo4j,” in 2015 6th International Confer- ence on Information, Intelligence, Systems and Applications (IISA), Jul. 2015, pp. 1–6.
  24. E. Curmi, R. Fenner, K. Richards, J. M. Allwood, B. Bajzˇelj, and G. M. Kopec, “Visualising a stochastic model of californian water resources using sankey diagrams,” Water Resources Management, vol. 27, no. 8, pp. 3035–3050, Jun. 2013. [Online]. Available: http://link.springer.com/10.1007/s11269-013-0331-2