1. Aksu, M.U., Bicakci, K., Dilek, M.H., Ozbayoglu, A.M., Tatli, E.ı.: Automated generation of attack graphs using NVD. In: Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy, pp. 135–142 (2018)
2. Doynikova, E., et al.: Security measuring system for IoT devices. In: Katsikas, S., et al. (eds.) Computer Security. ESORICS 2021 International Workshops: CyberICPS, SECPRE, ADIoT, SPOSE, CPS4CIP, and CDT &SECOMANE, Darmstadt, Germany, 4–8 October 2021, Revised Selected Papers, pp. 256–275. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95484-0_16
3. Edkrantz, M., Said, A.: Predicting exploit likelihood for cyber vulnerabilities with machine learning. Unpublished Master’s thesis, Chalmers Unıversıty of Technology, Department of Computer Science and Engineering, Gothenburg, Sweden, pp. 1–6 (2015)
4. Elbaz, C., Rilling, L., Morin, C.: Fighting N-day vulnerabilities with automated CVSS vector prediction at disclosure. In: Proceedings of the 15th International Conference on Availability, Reliability and Security, pp. 1–10 (2020)
5. Ferdiana, R., et al.: A systematic literature review of intrusion detection system for network security: Research trends, datasets and methods. In: 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), pp. 1–6. IEEE (2020)