A deep learning approach for detecting malicious JavaScript code.
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| Title: | A deep learning approach for detecting malicious JavaScript code. |
|---|---|
| Authors: | Wang, Yao, Cai, Wan-dong, Wei, Peng-cheng |
| Source: | Security & Communication Networks; 7/25/2016, Vol. 9 Issue 11, p1520-1534, 15p |
| Subject Terms: | JAVASCRIPT programming language, MACHINE learning, DATA transmission systems, LOGISTIC regression analysis |
| Abstract: | Malicious JavaScript code in webpages on the Internet is an emergent security issue because of its universality and potentially severe impact. Because of its obfuscation and complexities, detecting it has a considerable cost. Over the last few years, several machine learning-based detection approaches have been proposed; most of them use shallow discriminating models with features that are constructed with artificial rules. However, with the advent of the big data era for information transmission, these existing methods already cannot satisfy actual needs. In this paper, we present a new deep learning framework for detection of malicious JavaScript code, from which we obtained the highest detection accuracy compared with the control group. The architecture is composed of a sparse random projection, deep learning model, and logistic regression. Stacked denoising auto-encoders were used to extract high-level features from JavaScript code; logistic regression as a classifier was used to distinguish between malicious and benign JavaScript code. Experimental results indicated that our architecture, with over 27 000 labeled samples, can achieve an accuracy of up to 95%, with a false positive rate less than 4.2% in the best case. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
| Copyright of Security & Communication Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 116123715 RelevancyScore: 853 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 853.196594238281 |
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| Items | – Name: Title Label: Title Group: Ti Data: A deep learning approach for detecting malicious JavaScript code. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yao%22">Wang, Yao</searchLink><br /><searchLink fieldCode="AR" term="%22Cai%2C+Wan-dong%22">Cai, Wan-dong</searchLink><br /><searchLink fieldCode="AR" term="%22Wei%2C+Peng-cheng%22">Wei, Peng-cheng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Security & Communication Networks; 7/25/2016, Vol. 9 Issue 11, p1520-1534, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22JAVASCRIPT+programming+language%22">JAVASCRIPT programming language</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+transmission+systems%22">DATA transmission systems</searchLink><br /><searchLink fieldCode="DE" term="%22LOGISTIC+regression+analysis%22">LOGISTIC regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Malicious JavaScript code in webpages on the Internet is an emergent security issue because of its universality and potentially severe impact. Because of its obfuscation and complexities, detecting it has a considerable cost. Over the last few years, several machine learning-based detection approaches have been proposed; most of them use shallow discriminating models with features that are constructed with artificial rules. However, with the advent of the big data era for information transmission, these existing methods already cannot satisfy actual needs. In this paper, we present a new deep learning framework for detection of malicious JavaScript code, from which we obtained the highest detection accuracy compared with the control group. The architecture is composed of a sparse random projection, deep learning model, and logistic regression. Stacked denoising auto-encoders were used to extract high-level features from JavaScript code; logistic regression as a classifier was used to distinguish between malicious and benign JavaScript code. Experimental results indicated that our architecture, with over 27 000 labeled samples, can achieve an accuracy of up to 95%, with a false positive rate less than 4.2% in the best case. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Security & Communication Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/sec.1441 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1520 Subjects: – SubjectFull: JAVASCRIPT programming language Type: general – SubjectFull: MACHINE learning Type: general – SubjectFull: DATA transmission systems Type: general – SubjectFull: LOGISTIC regression analysis Type: general Titles: – TitleFull: A deep learning approach for detecting malicious JavaScript code. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Yao – PersonEntity: Name: NameFull: Cai, Wan-dong – PersonEntity: Name: NameFull: Wei, Peng-cheng IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 07 Text: 7/25/2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 19390114 Numbering: – Type: volume Value: 9 – Type: issue Value: 11 Titles: – TitleFull: Security & Communication Networks Type: main |
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