Matches in SemOpenAlex for { <https://semopenalex.org/work/W3203411727> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3203411727 abstract "The continuous discovery of software vulnerabilities have brought great challenges to the cyber security, which will lead to severe systematical or individual losses after being exploited. But the harshly increasing of software vulnerabilities overwhelms the time consuming vulnerability analysis. Security experts must pay more attention to the ones which have the highest priority to be repaired. In general, both severity and exploitability determine the severity of a software vulnerability. Compared with the severity evaluated by the Common Vulnerability Scoring System (CVSS score), the exploitability is still lack of a well-accepted standard. Furthermore, based on the perspective of attack and defense, we found that the exploitability of vulnerabilities is more attractive to hackers so that system or individual is severely affected by the exploitability rather than the severity. In this paper, we propose a deep learning based approach to predict the exploitability of the vulnerability by using the correlated textual description and characteristics. Specifically, our approach takes character-level Convolutional Neural Network (charCNN) to fetch more fine-grained character-level features from the vulnerability description instead of the word-level features considered by the previous literatures. And we highlight the importance of vulnerability characteristics such as Confidentiality Impact, Integrity Impact, Attack Vector etc. during the determination of vulnerability exploitability. Extensive experiments are set to prove the effectiveness of the given charCNN approach through the comparison on both different levels of features and different neural network models. Our approach achieves the best F1 values 93.1% (at least 2.2% more than the baselines). And we also investigate the efficiency of charCNN trained by historical vulnerability when predicting the exploitability of the newly published vulnerabilities. Finally, we further explore the robustness of the proposed model by changing the scale of training sets. For the prediction of vulnerability exploitability, we recommend to adopt 40.0% to 50.0% vulnerabilities to train a robust charCNN model." @default.
- W3203411727 created "2021-10-11" @default.
- W3203411727 creator A5003064538 @default.
- W3203411727 creator A5028641941 @default.
- W3203411727 creator A5053292340 @default.
- W3203411727 creator A5083729859 @default.
- W3203411727 creator A5083940267 @default.
- W3203411727 date "2021-08-01" @default.
- W3203411727 modified "2023-10-12" @default.
- W3203411727 title "A Character-Level Convolutional Neural Network for Predicting Exploitability of Vulnerability" @default.
- W3203411727 cites W1832693441 @default.
- W3203411727 cites W1999265552 @default.
- W3203411727 cites W2079753286 @default.
- W3203411727 cites W2166336492 @default.
- W3203411727 cites W2312398278 @default.
- W3203411727 cites W2513738415 @default.
- W3203411727 cites W2948226814 @default.
- W3203411727 cites W2982413960 @default.
- W3203411727 doi "https://doi.org/10.1109/tase52547.2021.00014" @default.
- W3203411727 hasPublicationYear "2021" @default.
- W3203411727 type Work @default.
- W3203411727 sameAs 3203411727 @default.
- W3203411727 citedByCount "2" @default.
- W3203411727 countsByYear W32034117272022 @default.
- W3203411727 crossrefType "proceedings-article" @default.
- W3203411727 hasAuthorship W3203411727A5003064538 @default.
- W3203411727 hasAuthorship W3203411727A5028641941 @default.
- W3203411727 hasAuthorship W3203411727A5053292340 @default.
- W3203411727 hasAuthorship W3203411727A5083729859 @default.
- W3203411727 hasAuthorship W3203411727A5083940267 @default.
- W3203411727 hasConcept C119857082 @default.
- W3203411727 hasConcept C137176749 @default.
- W3203411727 hasConcept C154945302 @default.
- W3203411727 hasConcept C15744967 @default.
- W3203411727 hasConcept C167063184 @default.
- W3203411727 hasConcept C172776598 @default.
- W3203411727 hasConcept C199360897 @default.
- W3203411727 hasConcept C2524010 @default.
- W3203411727 hasConcept C2777904410 @default.
- W3203411727 hasConcept C2780861071 @default.
- W3203411727 hasConcept C33923547 @default.
- W3203411727 hasConcept C38652104 @default.
- W3203411727 hasConcept C41008148 @default.
- W3203411727 hasConcept C542102704 @default.
- W3203411727 hasConcept C81363708 @default.
- W3203411727 hasConcept C86844869 @default.
- W3203411727 hasConcept C95713431 @default.
- W3203411727 hasConceptScore W3203411727C119857082 @default.
- W3203411727 hasConceptScore W3203411727C137176749 @default.
- W3203411727 hasConceptScore W3203411727C154945302 @default.
- W3203411727 hasConceptScore W3203411727C15744967 @default.
- W3203411727 hasConceptScore W3203411727C167063184 @default.
- W3203411727 hasConceptScore W3203411727C172776598 @default.
- W3203411727 hasConceptScore W3203411727C199360897 @default.
- W3203411727 hasConceptScore W3203411727C2524010 @default.
- W3203411727 hasConceptScore W3203411727C2777904410 @default.
- W3203411727 hasConceptScore W3203411727C2780861071 @default.
- W3203411727 hasConceptScore W3203411727C33923547 @default.
- W3203411727 hasConceptScore W3203411727C38652104 @default.
- W3203411727 hasConceptScore W3203411727C41008148 @default.
- W3203411727 hasConceptScore W3203411727C542102704 @default.
- W3203411727 hasConceptScore W3203411727C81363708 @default.
- W3203411727 hasConceptScore W3203411727C86844869 @default.
- W3203411727 hasConceptScore W3203411727C95713431 @default.
- W3203411727 hasLocation W32034117271 @default.
- W3203411727 hasOpenAccess W3203411727 @default.
- W3203411727 hasPrimaryLocation W32034117271 @default.
- W3203411727 hasRelatedWork W127241795 @default.
- W3203411727 hasRelatedWork W2002568488 @default.
- W3203411727 hasRelatedWork W2020865170 @default.
- W3203411727 hasRelatedWork W2089968953 @default.
- W3203411727 hasRelatedWork W2362011720 @default.
- W3203411727 hasRelatedWork W2394262443 @default.
- W3203411727 hasRelatedWork W2981941102 @default.
- W3203411727 hasRelatedWork W4206104612 @default.
- W3203411727 hasRelatedWork W4293696969 @default.
- W3203411727 hasRelatedWork W53551321 @default.
- W3203411727 isParatext "false" @default.
- W3203411727 isRetracted "false" @default.
- W3203411727 magId "3203411727" @default.
- W3203411727 workType "article" @default.