journal article·2023-11-09Open access

Vulnerability Exploitation Risk Assessment Based on Offensive Security Approach

Seong-Su Yoon, Do-Yeon Kim, Ka-Kyung Kim, Ieck-Chae Euom

Applied Sciences· Volume 13· Issue 22

Abstract

Security incidents targeting control systems and the industrial internet of things (IIoT) are on the rise as attackers gain a better understanding of the nature of these systems and their increasing connectivity to information technology (IT). Every year, the number of vulnerabilities associated with these incidents increases, making it impractical to apply timely patches for all of them. The current vulnerability assessments, which are the basis for vulnerability patching, have limitations in that they do not adequately reflect the risk of exploitation in the real world after discovery and do not consider operational technology (OT) and industrial control system (ICS) environments other than IT environments. This study proposes to evaluate exploit risk in real-world environments by considering OT/ICS environments and calculating three metrics, including exploit chain risk, exploit code availability, and exploit use probability based on cyber threat information, including IIoT vulnerability data, used in OT/ICS environments. In addition, we construct exploitation scenarios in a control system environment to prioritize vulnerabilities with a high risk of exploitation based on the three metrics. We show that by assessing the risk of attackers’ intentions and exploited technologies for attacks against IIoT devices in a control system environment, we can provide defenders with comprehensive attack risk information for proactive defense.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Information and Cyber Security · Network Security and Intrusion Detection · Software Reliability and Analysis Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

System Security Research Center, Chonnam National University, Gwangju 61186, Republic of Korea

Department of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea

Funding

Nuclear Safety and Security Commission (NSSC) of the Republic of Korea · Korean government (MSIT) · MSIT (Ministry of Science and ICT), Korea · IITP (Institute for Information & communications Technology Planning & Evaluation)

References (30)

Show source-provided references
1. Addressing Industry 4.0 Cybersecurity Challenges10.1109/EMR.2019.2927559 ↗
2. (2023, June 18). FIRST CVSS Documentation. Available online: https://www.first.org/cvss/specification-document.
3. Balsam, A., Nowak, M., Walkowski, M., Oko, J., and Sujecki, S. (2023, January 2–6). Analysis of CVSS Vulnerability Base Scores in the Context of Exploits’ Availability. Proceedings of the 2023 23rd International Conference on Transparent Optical Networks (ICTON), Bucharest, Romania.10.1109/ICTON59386.2023.10207394 ↗
4. A look at the time delays in CVSS vulnerability scoring10.1016/j.aci.2017.12.002 ↗
5. A Survey of IIoT Protocols: A Measure of Vulnerability Risk Analysis Based on CVSS
6. Changing the Paradigm of Control System Cybersecurity
7. IIoT Cybersecurity Risk Modeling for SCADA Systems10.1109/JIOT.2018.2822842 ↗
8. Continuous auditing and threat detection in multi-cloud infrastructure10.1016/j.cose.2020.102124 ↗
9. (2023, October 07). MITRE ATT&CK. Available online: https://attack.mitre.org/.
10. VULCON: A system for vulnerability prioritization, mitigation, and management10.1145/3196884 ↗
11. Elbaz, C., Rilling, L., and Morin, C. (2020, January 25–28). Fighting N-day vulnerabilities with automated CVSS vector prediction at disclosure. Proceedings of the 15th International Conference on Availability, Reliability and Security, Virtual.10.1145/3407023.3407038 ↗
12. Dissanayaka, A.M., Mengel, S., Gittner, L., and Khan, H. (2020, January 9–12). Vulnerability prioritization, root cause analysis, and mitigation of secure data analytic framework implemented with mongodb on singularity linux containers. Proceedings of the 4th International Conference on Compute and Data Analysis, Silicon Valley, CA, USA.10.1145/3388142.3388168 ↗
13. Vulnerability Modelling for Hybrid Industrial Control System Networks10.1007/s10723-020-09528-w ↗
14. Chen, H., Liu, R., Park, N., and Subrahmanian, V.S. (2019, January 4–8). Using twitter to predict when vulnerabilities will be exploited. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.10.1145/3292500.3330742 ↗
15. CAVP: A context-aware vulnerability prioritization model10.1016/j.cose.2022.102639 ↗
16. Singh, U.K., and Joshi, C. (2016, January 19–21). Quantitative security risk evaluation using CVSS metrics by estimation of frequency and maturity of exploit. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA.
17. Bulut, M.F., Adebayo, A., Sow, D., and Ocepek, S. (2022). Vulnerability prioritization: An offensive security approach. arXiv.
18. Suciu, O., Nelson, C., Lyu, Z., Bao, T., and Dumitraș, T. (, January 10–12). Expected exploitability: Predicting the development of functional vulnerability exploits. Proceedings of the 31st USENIX Security Symposium 2022, USENIX Security 22, Boston, MA, USA.
19. Edkrantz, M., Truvé, S., and Said, A. (2015, January 3–5). Predicting vulnerability exploits in the wild. Proceedings of the 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, New York, NY, USA.10.1109/CSCloud.2015.56 ↗
20. Jacobs, J., Romanosky, S., Suciu, O., Edwards, B., and Sarabi, A. (2023, January 3–7). Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights. Proceedings of the 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Delft, The Netherlands.10.1109/EuroSPW59978.2023.00027 ↗
21. Exploiting TTP Co-Occurrence via GloVe-Based Embedding with MITRE ATT&CK Framework10.1109/ACCESS.2023.3315121 ↗
22. An APT Attack Scoring Method Using MITRE ATT&CK
23. Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
24. (2023, October 27). MITRE Engenuity. Available online: https://mitre-engenuity.org/cybersecurity/center-for-threat-informed-defense/our-work/mapping-attck-to-cve-for-impact/.
25. (2023, October 07). NSA/CSS Technical Cyber Threat Framework v2, Available online: https://media.defense.gov/2019/Jul/16/2002158108/-1/-1/0/CTR_NSA-CSS-TECHNICAL-CYBER-THREAT-FRAMEWORK_V2.PDF.
26. (2023, October 27). CVE CNAs. Available online: https://www.cve.org/ProgramOrganization/CNAs.
27. Adebiyi, A., Arreymbi, J., and Imafidon, C. (2013, January 15–17). A neural network based security tool for analyzing software. Proceedings of the Doctoral Conference on Computing, Electrical and Industrial Systems, Costa de Caparica, Portugal.10.1007/978-3-642-37291-9_9 ↗
28. Mu, D., Cuevas, A., Yang, L., Hu, H., Xing, X., Mao, B., and Wang, G. (2018, January 15–17). Understanding the reproducibility of crowd-reported security vulnerabilities. Proceedings of the 27th {USENIX} Security Symposium, {USENIX} Security 18, Baltimore, MD, USA.
29. Dong, Y., Guo, W., Chen, Y., Xing, X., Zhang, Y., and Wang, G. (2019, January 14–16). Towards the detection of inconsistencies in public security vulnerability reports. Proceedings of the 28th {USENIX} Security Symposium ({USENIX} Security 19, Santa Clara, CA, USA.
30. (2023, October 07). SOC Prime Security. Available online: https://socprime.com/rs/rule/40ab8bdc-9c02-4f1f-b59d-3045f9b0d4e4.