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.
Publisher
Balkan Journal of Electrical & Computer Engineering (BAJECE)
Reference24 articles.
1. [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. [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. [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. [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. [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
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