AMASS: Automated Software Mass Customization via Feature Identification and Tailoring

Xue, Hongfa and Chen, Yurong and Venkataramani, Guru and Lan, Tian (2019) AMASS: Automated Software Mass Customization via Feature Identification and Tailoring. EAI Endorsed Transactions on Security and Safety, 6 (20). e4. ISSN 2032-9393

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Abstract

The rapid inflation of software features brings inefficiency and vulnerabilities into programs, resulting in an increased attack surface with a higher possibility of exploitation. In this paper, we propose a novel framework for automated software mass customization (AMASS), which automatically identifies program features from binaries, tailors and eliminates the features to create customized program binaries in accordance with user needs, in a fully unsupervised fashion. It enables us to modularize program features and efficiently create customized program binaries at large scale. Evaluation using real-world executables including OpenSSL and LibreOffice demonstrates that AMASS can create a wide range of customized binaries for diverse feature requirements, with an average 92.76% accuracy for feature/function identification and up to 67% reduction of program attack surface.

Item Type: Article
Uncontrolled Keywords: Program customization, Deep learning, Binary analysis
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 26 Mar 2021 14:00
Last Modified: 26 Mar 2021 14:00
URI: https://eprints.eudl.eu/id/eprint/2118

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