Defense against adversarial malware using robust classifier: DAM-ROC.
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| Title: | Defense against adversarial malware using robust classifier: DAM-ROC. |
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| Authors: | Selvaganapathy, Shymala Gowri1 (AUTHOR) shymalagowri@gmail.com, Sadasivam, Sudha2 (AUTHOR) |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Dec2022, Vol. 47 Issue 4, p1-49. 49p. |
| Subjects: | Anti-malware (Computer software), Bayesian analysis, Occupational retraining |
| Abstract: | Malware authors focus on deceiving and evading Anti Malware Engines (AME). Evasion attacks take in malware samples and modify those samples to by-pass ml based AME. Existing learning based anti-malware solutions are either too limited or insufficient to neutralize the threats arising from evasion attacks. This has necessitated a more comprehensive and robust solution. This research attempts to develop a secure learning framework entitled, damroc. The objective is to shield anti-malware entities against evasion attacks by making use of an adaptive adversarial training framework with novel retraining sample selector, (DAM-ROC OR) for dnn based learners. Usage of bnn along with possible quantification of predictive uncertainties is adapted. This generic framework, DAM-ROC is evaluated on benchmarked Android and Windows datasets to explore necessary trade-off between performance and robustness. DAM-ROC models are retrained to defend against gradient attacks like rBIMk, dBIMk, GRAMS and JSMA. Empirical results show that proposed DAM-ROC framework could increase robustness against multiple evasion attacks without compromising on performance when compared against two existing frameworks, SLEIPNIR and KBL. GRAMS retrained Bayesian models have demonstrated consistent performance against all considered attacks which is crucial for real world scenarios since it cannot be predicted in advance which attack will be deployed. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 159792348 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Defense against adversarial malware using robust classifier: DAM-ROC. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Selvaganapathy%2C+Shymala+Gowri%22">Selvaganapathy, Shymala Gowri</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shymalagowri@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sadasivam%2C+Sudha%22">Sadasivam, Sudha</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Dec2022, Vol. 47 Issue 4, p1-49. 49p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anti-malware+%28Computer+software%29%22">Anti-malware (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+retraining%22">Occupational retraining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Malware authors focus on deceiving and evading Anti Malware Engines (AME). Evasion attacks take in malware samples and modify those samples to by-pass ml based AME. Existing learning based anti-malware solutions are either too limited or insufficient to neutralize the threats arising from evasion attacks. This has necessitated a more comprehensive and robust solution. This research attempts to develop a secure learning framework entitled, damroc. The objective is to shield anti-malware entities against evasion attacks by making use of an adaptive adversarial training framework with novel retraining sample selector, (DAM-ROC OR) for dnn based learners. Usage of bnn along with possible quantification of predictive uncertainties is adapted. This generic framework, DAM-ROC is evaluated on benchmarked Android and Windows datasets to explore necessary trade-off between performance and robustness. DAM-ROC models are retrained to defend against gradient attacks like rBIMk, dBIMk, GRAMS and JSMA. Empirical results show that proposed DAM-ROC framework could increase robustness against multiple evasion attacks without compromising on performance when compared against two existing frameworks, SLEIPNIR and KBL. GRAMS retrained Bayesian models have demonstrated consistent performance against all considered attacks which is crucial for real world scenarios since it cannot be predicted in advance which attack will be deployed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=159792348 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12046-022-01980-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 49 StartPage: 1 Subjects: – SubjectFull: Anti-malware (Computer software) Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Occupational retraining Type: general Titles: – TitleFull: Defense against adversarial malware using robust classifier: DAM-ROC. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Selvaganapathy, Shymala Gowri – PersonEntity: Name: NameFull: Sadasivam, Sudha IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 47 – Type: issue Value: 4 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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