IoT-oriented high-efficient anti-malware hardware focusing on time series metadata extractable from inside a processor core.
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| Title: | IoT-oriented high-efficient anti-malware hardware focusing on time series metadata extractable from inside a processor core. |
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| Authors: | Koike, Kazuki1 (AUTHOR) em19005@g.kogakuin.jp, Kobayashi, Ryotaro1 (AUTHOR), Katoh, Masahiko2 (AUTHOR) |
| Source: | International Journal of Information Security. Aug2022, Vol. 21 Issue 4, p1-19. 19p. |
| Subjects: | Anti-malware (Computer software), Time series analysis, Metadata, Machine learning, Malware |
| Abstract: | We aim to improve the efficiency of our previously proposed anti-malware hardware; it is a hardware-implemented malware detection mechanism that uses information inside the processor. We previously evaluated a prototype, but, due to its prototypical nature, there remain limitations, such as only detecting certain behaviors, high power consumption, and a tendency to bloat the training model. In this paper, we propose a circuit and a learning method to achieve high efficiency, low power consumption, and light weight for the model. In considering these three issues, we focus on time-series metadata obtained by transforming the processor information. To improve efficiency, we implement predictive detection to predict the behavior of metadata in the malware detection component. This lets the model detect malware within less than 19% of the number of execution cycles of the conventional method. To reduce power consumption, we implement a sampling circuit that interrupts the input to the detection circuit at regular intervals, reducing the system's uptime by 99% while maintaining judgment accuracy. Finally, for a light weight, we focus on the training process of the metadata generator based on a machine-learning model. By applying sampling learning and feature dimensionality reduction in the training process, a metadata generator approximately 16% smaller than the previous version is created. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Information Security 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: 158205761 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: IoT-oriented high-efficient anti-malware hardware focusing on time series metadata extractable from inside a processor core. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Koike%2C+Kazuki%22">Koike, Kazuki</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> em19005@g.kogakuin.jp</i><br /><searchLink fieldCode="AR" term="%22Kobayashi%2C+Ryotaro%22">Kobayashi, Ryotaro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Katoh%2C+Masahiko%22">Katoh, Masahiko</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Information+Security%22">International Journal of Information Security</searchLink>. Aug2022, Vol. 21 Issue 4, p1-19. 19p. – 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="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We aim to improve the efficiency of our previously proposed anti-malware hardware; it is a hardware-implemented malware detection mechanism that uses information inside the processor. We previously evaluated a prototype, but, due to its prototypical nature, there remain limitations, such as only detecting certain behaviors, high power consumption, and a tendency to bloat the training model. In this paper, we propose a circuit and a learning method to achieve high efficiency, low power consumption, and light weight for the model. In considering these three issues, we focus on time-series metadata obtained by transforming the processor information. To improve efficiency, we implement predictive detection to predict the behavior of metadata in the malware detection component. This lets the model detect malware within less than 19% of the number of execution cycles of the conventional method. To reduce power consumption, we implement a sampling circuit that interrupts the input to the detection circuit at regular intervals, reducing the system's uptime by 99% while maintaining judgment accuracy. Finally, for a light weight, we focus on the training process of the metadata generator based on a machine-learning model. By applying sampling learning and feature dimensionality reduction in the training process, a metadata generator approximately 16% smaller than the previous version is created. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Information Security 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=158205761 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10207-021-00577-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Anti-malware (Computer software) Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Metadata Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Malware Type: general Titles: – TitleFull: IoT-oriented high-efficient anti-malware hardware focusing on time series metadata extractable from inside a processor core. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Koike, Kazuki – PersonEntity: Name: NameFull: Kobayashi, Ryotaro – PersonEntity: Name: NameFull: Katoh, Masahiko IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 16155262 Numbering: – Type: volume Value: 21 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Information Security Type: main |
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