Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1537
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTellenbach, Bernhard-
dc.contributor.authorPaganoni, Sergio-
dc.contributor.authorRennhard, Marc-
dc.date.accessioned2017-11-29T08:49:13Z-
dc.date.available2017-11-29T08:49:13Z-
dc.date.issued2016-
dc.identifier.issn1942-2636de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/1601-
dc.description.abstractJavaScript is a common attack vector to probe for known vulnerabilities to select a fitting exploit or to manipulate the Document Object Model (DOM) of a web page in a harmful way. The JavaScripts used in such attacks are often obfuscated to make them hard to detect using signature-based approaches. On the other hand, since the only legitimate reason to obfuscate a script is to protect intellectual property, there are not many scripts that are both benign and obfuscated. A detector that can reliably detect obfuscated JavaScripts would therefore be a valuable tool in fighting JavaScript based attacks. In this paper, we compare the performance of nine different classifiers with respect to correctly classifying obfuscated and non-obfuscated scripts. For our experiments, we use a data set of regular, minified, and obfuscated samples from jsDeliver and the Alexa top 5000 websites and a set of malicious samples from MELANI. We find that the best of these classifiers, the boosted decision tree classifier, performs very well to correctly classify obfuscated and non-obfuscated scripts with precision and recall rates of around 99 percent. The boosted decision tree classifier is then used to assess how well this approach can cope with scripts obfuscated by an obfuscator not present in our training set. The results show that while it may work for some obfuscators, it is still critical to have as many different obfuscators in the training set as possible. Finally, we describe the results from experiments to classify malicious obfuscated scripts when no such scripts are included in the training set. Depending on the set of features used, it is possible to detect about half of those scripts, even though those samples do not seem to use any of the obfuscators used in our training set.de_CH
dc.language.isoende_CH
dc.publisherIARIAde_CH
dc.relation.ispartofInternational Journal on Advances in Securityde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMachine Learningde_CH
dc.subjectJavaScript obfuscationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDetecting obfuscated JavaScripts from known and unknown obfuscators using machine learningde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
dc.identifier.doi10.21256/zhaw-1537-
zhaw.funding.euNode_CH
zhaw.issue3/4de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end206de_CH
zhaw.pages.start196de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume9de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedInformation Securityde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2017_Tellenbach_Detecting_obfuscated_JavaScripts_Advance_in_Securtiy.pdf210.91 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.