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The EPSS Model

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Saul, Stefan Savage, and Geoffrey M. Voelker, (2010) Beyond Heuristics: Learning to Classify Vulnerabilities and Predict Exploits. Available at <a href="http://cseweb.ucsd.edu/~saul/papers/kdd10_exploit.pdf">http://cseweb.ucsd.edu/~saul/papers/kdd10_exploit.pdf</a>.</li> <li>Michel Edkrantz and Alan Said. Predicting cyber vulnerability exploits with machine learning. In SCAI, pages 48–57, 2015.</li> <li>Carl Sabottke, Octavian Suciu, and Tudor Dumitras, Vulnerability disclosure in the age of social media: exploiting twitter for predicting real-world exploits. In 24th USENIX Security Symposium (USENIX Security 15), pages 1041–1056, 2015.</li> <li>Mohammed Almukaynizi, Eric Nunes, Krishna Dharaiya, Manoj Senguttuvan, Jana Shakarian, and Paulo Shakarian. Proactive identification of exploits in the wild through vulnerability mentions online. In 2017 International Conference on Cyber Conflict (CyCon US), pages 82–88. IEEE, 2017.</li> <li>Benjamin L. Bullough, Anna K. Yanchenko, Christopher L. Smith, and Joseph R. Zipkin. 2017. Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data. In Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics (IWSPA '17). Association for Computing Machinery, New York, NY, USA, 45–53. <a href="https://doi.org/10.1145/3041008.3041009a">https://doi.org/10.1145/3041008.3041009a</a></li> <li>Reinthal, A., Filippakis, E.L., Almgren, M. (2018). Data Modelling for Predicting Exploits. In: Gruschka, N. (eds) Secure IT Systems. NordSec 2018. Lecture Notes in Computer Science(), vol 11252. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-03638-6_21">https://doi.org/10.1007/978-3-030-03638-6_21</a></li> <li>Haipeng Chen, Rui Liu, Noseong Park, and V.S. Subrahmanian. 2019. Using Twitter to Predict When Vulnerabilities will be Exploited. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 3143–3152. <a href="https://doi.org/10.1145/3292500.3330742">https://doi.org/10.1145/3292500.3330742</a></li> <li>Nazgol Tavabi, Palash Goyal, Mohammed Almukaynizi, Paulo Shakarian, and Kristina Lerman. 2018. Darkembed: Exploit prediction with neural language models. In AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI).</li> <li>Chaowei Xiao, Armin Sarabi, Yang Liu, Bo Li, Mingyan Liu, and Tudor Dumitras. 2018. From patching delays to infection symptoms: Using risk profiles for an early discovery of vulnerabilities exploited in the wild. In 27th {USENIX} Security Symposium ({USENIX} Security’18). 903–918.</li> <li>Kenneth Alperin, Allan Wollaber, Dennis Ross, Pierre Trepagnier, and Leslie Leonard. 2019. Risk Prioritization by Leveraging Latent Vulnerability Features in a Contested Environment. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec'19). Association for Computing Machinery, New York, NY, USA, 49–57. <a href="https://doi.org/10.1145/3338501.3357365">https://doi.org/10.1145/3338501.3357365</a></li> <li>Fang Y, Liu Y, Huang C, Liu L. FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm. PLoS One. 2020 Feb 6;15(2):e0228439. doi: 10.1371/journal.pone.0228439. PMID: 32027693; PMCID: PMC7004314.</li> <li>Hoque, Mohammad Shamsul, Norziana Jamil, Nowshad Amin and Kwok-Yan Lam. “An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding.” Sensors (Basel, Switzerland) 21 (2021): n. Pag.</li> <li>Bhatt, N, Anand, A, Yadavalli, VSS. Exploitability prediction of software vulnerabilities. Qual Reliab Engng Int. 2021; 37: 648– 663. <a href="https://doi.org/10.1002/qre.2754">https://doi.org/10.1002/qre.2754</a></li> </ol> <h2 id="Attack-Prediction">Attack Prediction</h2> <ol> <li>S. Mathew, D. Britt, R. Giomundo, S. 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Optimal policies for security patch management. INFORMS Journal on Computing, 27(3), 462-477.</li> <li>August, T., Dao, D., &amp; Kim, K. (2019). Market segmentation and software security: Pricing patching rights. Management Science. In Press.</li> <li>Kenna Security and Cyentia Institute. Prioritization to prediction, volume 3. Technical report, Kenna Security, July 2019.</li> <li>Allodi, L., and Massacci, F, (2014). Comparing Vulnerability Severity and Exploits Using Case-Control Studies. ACM Trans. Inf. Syst. Secur. 17, 1, Article 1 (August 2014), 20 pages. DOI:<a href="https://doi.org/10.1145/2630069">https://doi.org/10.1145/2630069</a></li> <li>Allodi L. (2015) The Heavy Tails of Vulnerability Exploitation. In: Piessens F., Caballero J., Bielova N. (eds) Engineering Secure Software and Systems. ESSoS 2015. Lecture Notes in Computer Science, vol 8978. 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Information Systems Research, 26(3):565–584, 2015.</li> <li>Boechat, F., Ribas, G., Senos, L., Bicudo, M., Nogueira, M. S., de Aguiar, L. P., &amp; Menasche, D. S. (2021). Is Vulnerability Report Confidence Redundant? Pitfalls Using Temporal Risk Scores. IEEE Security &amp; Privacy, (01), 2-11.</li> </ol> <h2 id="Vulnerability-Modeling">Vulnerability Modeling</h2> <ol> <li>Afsah Anwar, Ahmed Abusnaina, Songqing Chen, Frank Li, David Mohaisen, (2020) Cleaning the NVD: Comprehensive Quality Assessment, Improvements, and Analyses, available at arXiv:2006.15074v1.</li> <li>Miranda, L., Vieira, D., de Aguiar, L. P., Menasche, D. S., Bicudo, M., Nogueira, M., ... &amp; Lovat, E. (2021). On the Flow of Software Security Advisories. IEEE Transactions on Network and Service Management.</li> <li>R. A. Miura-Ko and N. 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Automatic Keyword Extraction from Individual Documents. Text Mining: Applications and Theory. 1 - 20. 10.1002/9780470689646.ch1.</li> <li>Alfredo Vellido, José David Martín-Guerrero, and Paulo JG Lisboa. Making machine learning models interpretable. In ESANN, volume 12, pages 163–172. Citeseer, 2012.</li> <li>Federico Cabitza, Raffaele Rasoini, and Gian Franco Gensini. Unintended consequences of machine learning in medicine. Jama, 318(6):517–518, 2017.</li> <li>Jonathan H Chen and StevenMAsch. Machine learning and prediction in medicine—beyond the peak of inflated expectations. The New England journal of medicine, 376(26):2507, 2017</li> <li>Chen, T., Guestrin, C., (2016) XGBoost: A Scalable Tree Boosting System, KDD ’16, San Francisco, CA. Available at <a href="https://arxiv.org/abs/1603.02754">https://arxiv.org/abs/1603.02754</a>. Last accessed February 16, 2019.</li> <li>Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608, 2017.</li> </ol></div></div><div id="navbar" data-studio="CU52CV1W8g"><div id="c4" data-studio="Yu8FjCC11g"><ul class="navbar"><li><a href="/epss">Exploit Prediction Scoring System (EPSS)</a><ul><li><a href="/epss/model">The EPSS Model</a></li><li><a href="/epss/data_stats">Data and Statistics</a></li><li><a href="/epss/user-guide">User Guide</a></li><li><a href="/epss/research">EPSS Research and Presentations</a></li><li><a href="/epss/faq">Frequently Asked Questions</a></li><li><a href="/epss/who_is_using">Who is using EPSS?</a></li><li><a href="/epss/epss_tools">Open-source EPSS Tools</a></li><li><a href="/epss/api">API</a></li><li><a href="/epss/papers">Related Exploit Research</a></li><li><a>Blog</a><ul><li><a href="/epss/articles/prob_percentile_bins">Understanding EPSS Probabilities and Percentiles</a></li><li><a href="/epss/articles/log4shell">Log4Shell Use Case</a></li><li><a href="/epss/articles/estimating_old_cvss">Estimating CVSS v3 Scores for 100,000 Older Vulnerabilities</a></li></ul></li><li><a href="/epss/partners">Data Partners</a></li></ul></li></ul></div></div><div id="sidebar" data-studio="CU52CV1W8g"></div><footer><div id="footer" data-studio="CU52CV1W8g"><div id="c2" data-studio="Yu8FjCC11g"><div class="content"> <div class="support"> <div class="kbsearch bottom"> <p><a href="https://support.first.org"><img src="/_/img/icon-portal_support.svg" alt="FIRST Support" title="FIRST Support" /></a> <input class="kb-search" type="search" placeholder="Do you need help?"></p> </div> </div> <div id="socialnetworks"><a href="/about/sdg" title="FIRST Supported Sustainable Development Goals (SDG)" class="icon-sdg"></a><a rel="me" href="https://bsky.app/profile/first.org" target="_blank" title="BlueSky @first.org" class="icon-bluesky"></a><a rel="me" href="https://infosec.exchange/@firstdotorg" target="_blank" title="@FIRSTdotOrg@infosec.exchange" class="icon-mastodon"></a><a href="https://twitter.com/FIRSTdotOrg" target="_blank" title="Twitter @FIRSTdotOrg" class="icon-tw"></a><a href="https://www.linkedin.com/company/firstdotorg" target="_blank" title="FIRST.Org at LinkedIn" class="icon-linkedin"></a><a href="https://www.facebook.com/FIRSTdotorg" target="_blank" title="FIRST.Org at Facebook" class="icon-fb"></a><a href="https://github.com/FIRSTdotorg" target="_blank" title="FIRST.Org at Github" class="icon-github"></a><a href="https://www.youtube.com/c/FIRSTdotorg" target="_blank" title="FIRST.Org at Youtube" class="icon-youtube"></a><a href="/podcasts" title="FIRST.Org Podcasts" class="icon-podcast"></a></div> <p><a href="/copyright">Copyright</a> © 2015—2025 by Forum of Incident Response and Security Teams, Inc. 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