Optimizing TEE Protection by Automatically Augmenting Requirements Specifications
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An increasing number of software systems must safeguard their confidential data and code, referred to as critical program information (CPI). Such safeguarding is commonly accomplished by isolating CPI in a trusted execution environment (TEE), with the isolated CPI becoming a trusted computing base (TCB). TEE protection incurs heavy performance costs, as TEE-based functionality is expensive to both invoke and execute. Despite these costs, projects that use TEEs tend to have unnecessarily large TCBs. As based on our analysis, developers often put code and data into TEE for convenience rather than protection reasons, thus not only compromising performance but also reducing the effectiveness of TEE protection. In order for TEEs to provide maximum benefits for protecting CPI, their usage must be systematically incorporated into the entire software engineering process, starting from Requirements Engineering. To address this problem, we present a novel approach that incorporates TEEs in the Requirements Engineering phase by using natural language processing (NLP) to classify those software requirements that are security critical and should be isolated in TEE. Our approach takes as input a requirements specification and outputs a list of annotated software requirements. The annotations recommend to the developer which corresponding features comprise CPI that should be protected in a TEE. Our evaluation results indicate that our approach identifies CPI with a high degree of accuracy to incorporate safeguarding CPI into Requirements Engineering.
General Audience Abstract
An increasing number of software systems must safeguard their confidential data like passwords, payment information, personal details, etc. This confidential information is commonly protected using a Trusted Execution Environment (TEE), an isolated environment provided by either the existing processor or separate hardware that interacts with the operating system to secure sensitive data and code. Unfortunately, TEE protection incurs heavy performance costs, with TEEs being slower than modern processors and frequent communication between the system and the TEE incurring heavy performance overhead. We discovered that developers often put code and data into TEE for convenience rather than protection purposes, thus not only hurting performance but also reducing the effectiveness of TEE protection. By thoroughly examining a project's features in the Requirements Engineering phase, which defines the project's functionalities, developers would be able to understand which features handle confidential data. To that end, we present a novel approach that incorporates TEEs in the Requirements Engineering phase by means of Natural Language Processing (NLP) tools to categorize the project requirements that may warrant TEE protection. Our approach takes as input a project's requirements and outputs a list of categorized requirements defining which requirements are likely to make use of confidential information. Our evaluation results indicate that our approach performs this categorization with a high degree of accuracy to incorporate safeguarding the confidentiality related features in the Requirements Engineering phase.
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