reAnalyst：可扩展的逆向工程活动标注
reAnalyst: Scalable Annotation of Reverse Engineering Activities
AI总结：
本文提出 reAnalyst 框架，通过跨工具采集截图、按键和进程等数据并进行半自动标注，解决逆向工程研究中人工收集与主观分析的问题，实验和调查验证了其识别能力、可接受性与实用性。
摘要：
本文介绍了 reAnalyst，这是一个旨在通过对各种逆向工程（RE）工具中的 RE 活动进行半自动化标注，从而促进逆向工程实践研究的框架。通过在 RE 实验期间整合与工具无关的截图、按键、活动进程及其他类型数据的数据收集，并结合半自动化数据分析和标注生成，reAnalyst 旨在克服传统 RE 研究严重依赖人工数据收集和主观分析的局限。该框架能够实现更高效的数据分析，进而使研究人员能够更全面、更高效地探索逆向工程师所使用保护技术和策略的有效性。实验评估验证了该框架从复杂度各异的多种截图中识别 RE 活动的能力。基于本框架对以往实验的观察，以及在逆向工程师中开展的一项调查，进一步证明了该方法的可接受性和实用性。
英文摘要：
This paper introduces reAnalyst, a framework designed to facilitate the study of reverse engineering (RE) practices through the semi-automated annotation of RE activities across various RE tools. By integrating tool-agnostic data collection of screenshots, keystrokes, active processes, and other types of data during RE experiments with semi-automated data analysis and generation of annotations, reAnalyst aims to overcome the limitations of traditional RE studies that rely heavily on manual data collection and subjective analysis. The framework enables more efficient data analysis, which will in turn allow researchers to explore the effectiveness of protection techniques and strategies used by reverse engineers more comprehensively and efficiently. Experimental evaluations validate the framework's capability to identify RE activities from a diverse range of screenshots with varied complexities. Observations on past experiments with our framework as well as a survey among reverse engineers provide further evidence of the acceptability and practicality of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
