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• W3004437357 endingPage "e0228439" @default.
• W3004437357 startingPage "e0228439" @default.
• W3004437357 abstract "In recent years, the number of vulnerabilities discovered and publicly disclosed has shown a sharp upward trend. However, the value of exploitation of vulnerabilities varies for attackers, considering that only a small fraction of vulnerabilities are exploited. Therefore, the realization of quick exclusion of the non-exploitable vulnerabilities and optimal patch prioritization on limited resources has become imperative for organizations. Recent works using machine learning techniques predict exploited vulnerabilities by extracting features from open-source intelligence (OSINT). However, in the face of explosive growth of vulnerability information, there is room for improvement in the application of past methods to multiple threat intelligence. A more general method is needed to deal with various threat intelligence sources. Moreover, in previous methods, traditional text processing methods were used to deal with vulnerability related descriptions, which only grasped the static statistical characteristics but ignored the context and the meaning of the words of the text. To address these challenges, we propose an exploit prediction model, which is based on a combination of fastText and LightGBM algorithm and called fastEmbed. We replicate key portions of the state-of-the-art work of exploit prediction and use them as benchmark models. Our model outperforms the baseline model whether in terms of the generalization ability or the prediction ability without temporal intermixing with an average overall improvement of 6.283% by learning the embedding of vulnerability-related text on extremely imbalanced data sets. Besides, in terms of predicting the exploits in the wild, our model also outperforms the baseline model with an F1 measure of 0.586 on the minority class (33.577% improvement over the work using features from darkweb/deepweb). The results demonstrate that the model can improve the ability to describe the exploitability of vulnerabilities and predict exploits in the wild effectively." @default.
• W3004437357 created "2020-02-14" @default.
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• W3004437357 date "2020-02-06" @default.
• W3004437357 modified "2023-10-16" @default.
• W3004437357 title "FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm" @default.
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• W3004437357 doi "https://doi.org/10.1371/journal.pone.0228439" @default.
• W3004437357 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7004314" @default.
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