Classifying Malicious Documents on the Basis of Plain-Text Features: Problem, Solution, and Experiences
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Jiwon | - |
dc.contributor.author | Jeong, Dongho | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-06T06:23:23Z | - |
dc.date.available | 2022-07-06T06:23:23Z | - |
dc.date.created | 2022-05-04 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138950 | - |
dc.description.abstract | Cyberattacks widely occur by using malicious documents. A malicious document is an electronic document containing malicious codes along with some plain-text data that is human-readable. In this paper, we propose a novel framework that takes advantage of such plaintext data to determine whether a given document is malicious. We extracted plaintext features from the corpus of electronic documents and utilized them to train a classification model for detecting malicious documents. Our extensive experimental results with different combinations of three well-known vectorization strategies and three popular classification methods on five types of electronic documents demonstrate that our framework provides high prediction accuracy in detecting malicious documents. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Classifying Malicious Documents on the Basis of Plain-Text Features: Problem, Solution, and Experiences | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.3390/app12084088 | - |
dc.identifier.scopusid | 2-s2.0-85129215990 | - |
dc.identifier.wosid | 000786113800001 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.12, no.8, pp.1 - 13 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 12 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordAuthor | malware | - |
dc.subject.keywordAuthor | malicious document | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | text analysis | - |
dc.identifier.url | https://www.mdpi.com/2076-3417/12/8/4088 | - |
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