Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313033657> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4313033657 endingPage "104" @default.
- W4313033657 startingPage "89" @default.
- W4313033657 abstract "In an increasingly complex cyber environment, where the role of traditional protection tools is increasingly limited, intelligence is the key point in the battle. Through the information monitoring of Internet social platforms, potential cyberattack threats to enterprises, governments, and other institutions could be analyzed. Twitter, the world’s largest social media platform, spreads news and shares tweets about cybersecurity-related events and technologies daily, with cross-site scripting attacks being one of them. In the status quo, this paper proposes a cross-site scripting threat intelligence detection model based on deep learning, which can detect tweets involving threats related to cross-site scripting attacks. We utilized a variety of word vector extraction tools blended with topic word extraction techniques to construct a word vector matrix with multi-dimensional features. Then, the threat event detection model is trained using a bidirectional recurrent convolutional neural network with a self-attentive mechanism. In the experiment, the accuracy rate of our proposed model exceeds 0.96, and through multiple sets of control experimental data results, it is proved that the structure designed in the model is conducive to improving the performance of the model and that the model is effective in detecting tweets that involve cross-site scripting threats." @default.
- W4313033657 created "2023-01-06" @default.
- W4313033657 creator A5003561140 @default.
- W4313033657 creator A5024278894 @default.
- W4313033657 creator A5038506863 @default.
- W4313033657 date "2022-01-01" @default.
- W4313033657 modified "2023-10-16" @default.
- W4313033657 title "Cross-site Scripting Threat Intelligence Detection Based on Deep Learning" @default.
- W4313033657 cites W2079735306 @default.
- W4313033657 cites W2250539671 @default.
- W4313033657 cites W2578831221 @default.
- W4313033657 cites W2743411104 @default.
- W4313033657 cites W2810069506 @default.
- W4313033657 cites W2914662937 @default.
- W4313033657 cites W2925209208 @default.
- W4313033657 cites W2940125701 @default.
- W4313033657 cites W2999706762 @default.
- W4313033657 cites W2999814310 @default.
- W4313033657 cites W3034885317 @default.
- W4313033657 cites W3091919865 @default.
- W4313033657 cites W3134902663 @default.
- W4313033657 cites W4298872063 @default.
- W4313033657 doi "https://doi.org/10.1007/978-981-19-8445-7_6" @default.
- W4313033657 hasPublicationYear "2022" @default.
- W4313033657 type Work @default.
- W4313033657 citedByCount "0" @default.
- W4313033657 crossrefType "book-chapter" @default.
- W4313033657 hasAuthorship W4313033657A5003561140 @default.
- W4313033657 hasAuthorship W4313033657A5024278894 @default.
- W4313033657 hasAuthorship W4313033657A5038506863 @default.
- W4313033657 hasConcept C110875604 @default.
- W4313033657 hasConcept C111919701 @default.
- W4313033657 hasConcept C119857082 @default.
- W4313033657 hasConcept C136764020 @default.
- W4313033657 hasConcept C154945302 @default.
- W4313033657 hasConcept C162324750 @default.
- W4313033657 hasConcept C199360897 @default.
- W4313033657 hasConcept C2522767166 @default.
- W4313033657 hasConcept C2776748549 @default.
- W4313033657 hasConcept C2780801425 @default.
- W4313033657 hasConcept C34447519 @default.
- W4313033657 hasConcept C38652104 @default.
- W4313033657 hasConcept C41008148 @default.
- W4313033657 hasConcept C518677369 @default.
- W4313033657 hasConcept C61423126 @default.
- W4313033657 hasConcept C81363708 @default.
- W4313033657 hasConcept C95713431 @default.
- W4313033657 hasConceptScore W4313033657C110875604 @default.
- W4313033657 hasConceptScore W4313033657C111919701 @default.
- W4313033657 hasConceptScore W4313033657C119857082 @default.
- W4313033657 hasConceptScore W4313033657C136764020 @default.
- W4313033657 hasConceptScore W4313033657C154945302 @default.
- W4313033657 hasConceptScore W4313033657C162324750 @default.
- W4313033657 hasConceptScore W4313033657C199360897 @default.
- W4313033657 hasConceptScore W4313033657C2522767166 @default.
- W4313033657 hasConceptScore W4313033657C2776748549 @default.
- W4313033657 hasConceptScore W4313033657C2780801425 @default.
- W4313033657 hasConceptScore W4313033657C34447519 @default.
- W4313033657 hasConceptScore W4313033657C38652104 @default.
- W4313033657 hasConceptScore W4313033657C41008148 @default.
- W4313033657 hasConceptScore W4313033657C518677369 @default.
- W4313033657 hasConceptScore W4313033657C61423126 @default.
- W4313033657 hasConceptScore W4313033657C81363708 @default.
- W4313033657 hasConceptScore W4313033657C95713431 @default.
- W4313033657 hasLocation W43130336571 @default.
- W4313033657 hasOpenAccess W4313033657 @default.
- W4313033657 hasPrimaryLocation W43130336571 @default.
- W4313033657 hasRelatedWork W2337926734 @default.
- W4313033657 hasRelatedWork W2351571780 @default.
- W4313033657 hasRelatedWork W2748952813 @default.
- W4313033657 hasRelatedWork W2766405666 @default.
- W4313033657 hasRelatedWork W3027997911 @default.
- W4313033657 hasRelatedWork W4287776258 @default.
- W4313033657 hasRelatedWork W4312501200 @default.
- W4313033657 hasRelatedWork W4313050734 @default.
- W4313033657 hasRelatedWork W4366224123 @default.
- W4313033657 hasRelatedWork W1936400974 @default.
- W4313033657 isParatext "false" @default.
- W4313033657 isRetracted "false" @default.
- W4313033657 workType "book-chapter" @default.