Academic Journal
APIRO: A Framework for Automated Security Tools API Recommendation.
| Τίτλος: | APIRO: A Framework for Automated Security Tools API Recommendation. |
|---|---|
| Συγγραφείς: | SWORNA, ZARRIN TASNIM, ISLAM, CHADNI, BABAR, MUHAMMAD ALI |
| Πηγή: | ACM Transactions on Software Engineering & Methodology; Jan2023, Vol. 32 Issue 1, p1-42, 42p |
| Θεματικοί όροι: | Convolutional neural networks, Application program interfaces, Automatic identification, Data augmentation, Data security |
| Περίληψη: | Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team’s ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API Recommendation for security Orchestration, automation, and response, APIRO. To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top three relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using three security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy. Compared to the state-of-the-art baseline, APIRO is 26.93%, 23.03%, and 20.87% improved in terms of Top-1, Top-2, and Top-3 Accuracy and outperforms the baseline by 23.7% in terms of Mean Reciprocal Rank (MRR). [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Transactions on Software Engineering & Methodology is the property of Association for Computing Machinery 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: APIRO: A Framework for Automated Security Tools API Recommendation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22SWORNA%2C+ZARRIN+TASNIM%22">SWORNA, ZARRIN TASNIM</searchLink><br /><searchLink fieldCode="AR" term="%22ISLAM%2C+CHADNI%22">ISLAM, CHADNI</searchLink><br /><searchLink fieldCode="AR" term="%22BABAR%2C+MUHAMMAD+ALI%22">BABAR, MUHAMMAD ALI</searchLink> – Name: TitleSource Label: Source Group: Src Data: ACM Transactions on Software Engineering & Methodology; Jan2023, Vol. 32 Issue 1, p1-42, 42p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Application+program+interfaces%22">Application program interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+identification%22">Automatic identification</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team’s ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API Recommendation for security Orchestration, automation, and response, APIRO. To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top three relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using three security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy. Compared to the state-of-the-art baseline, APIRO is 26.93%, 23.03%, and 20.87% improved in terms of Top-1, Top-2, and Top-3 Accuracy and outperforms the baseline by 23.7% in terms of Mean Reciprocal Rank (MRR). [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ACM Transactions on Software Engineering & Methodology is the property of Association for Computing Machinery 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: 10.1145/3512768 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 42 StartPage: 1 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Application program interfaces Type: general – SubjectFull: Automatic identification Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Data security Type: general Titles: – TitleFull: APIRO: A Framework for Automated Security Tools API Recommendation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: SWORNA, ZARRIN TASNIM – PersonEntity: Name: NameFull: ISLAM, CHADNI – PersonEntity: Name: NameFull: BABAR, MUHAMMAD ALI IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 1049331X Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: ACM Transactions on Software Engineering & Methodology Type: main |
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