首页|基于函数调用指令特征分析的固件指令集架构识别方法

基于函数调用指令特征分析的固件指令集架构识别方法

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不同的固件常采用不同的指令集架构,固件指令集架构的识别是对嵌入式固件进行逆向分析和漏洞挖掘的基础.现有研究和相关工具在针对特定类型的嵌入式设备固件指令集架构识别时存在识别正确率低、误报率高的情况.针对上述问题,提出了 一种基于函数调用指令特征分析的固件指令集架构识别方法,通过同时利用指令中操作码和操作数所包含的信息识别目标固件中的函数调用指令,将其作为关键特征实现对不同指令集架构的分类,并基于该方法开发了原型系统EDFIR(Embed-ded Device Firmware Instruction set Recognizer).实验结果表明,相比 IDAPro,Ghidra,Radare2,Binwalk 以及 ISAdetect 这些当前应用最广泛和最新的工作,该方法具有更高的识别正确率、更低的误报率并具备更强的抗干扰能力,其对1000个真实设备固件的识别正确率高达97.9%,比目前识别效果最好的ISAdetect提升了 42.5%.此外,相关实验还证明,即使将分析规模缩小至完整固件的1/50,所提方法仍能保持95.31%的识别正确率,具有良好的识别性能.
Function-call Instruction Characteristic Analysis Based Instruction Set Architecture Recognization Method for Firmwares
The recognition of instruction set architecture is a crucial task for conducting security research on embedded devices,and has significant implications.However,existing studies and tools often suffer from low recognition accuracy and high false positive rates when identifying the firmware instruction set architecture of specific types of embedded devices.To address this is-sue,a new method for recognizing firmware instruction set architecture based on feature analysis of function call instructions is proposed.It identifies function call instructions in the target firmware by simultaneously utilizing the information contained in the operation codes and operands of the instructions,and uses them as key features to classify different instruction set architectures.A prototype system called EDFIR(embedded device firmware instruction set recognizer)has been developed based on this me-thod.Experimental results show that compared to currently widely used and state-of-the-art tools such as IDA Pro,Ghidra,Rada-re2,Binwalk,and ISA detect,the proposed method has higher recognition accuracy,lower false positive rates,and stronger anti-interference capabilities.It achieves a recognition accuracy of 97.9%on 1000 real device firmwares,which is 42.5%higher than the best performing ISA detect.Furthermore,experiments demonstrate that even when the analysis scale is reduced to 1/50 of the complete firmware,it can still maintain a recognition accuracy of 95.31%,indicating an excellent recognition performance.

Instruction set architectureClassification techniquesReverse analysis engineeringEmbedded device securityStatic analysis

贾凡、尹小康、盖贤哲、蔡瑞杰、刘胜利

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信息工程大学网络空间安全教育部重点实验室 郑州 450001

指令集架构 分类技术 逆向分析技术 嵌入式设备安全 静态分析技术

2024

计算机科学
重庆西南信息有限公司(原科技部西南信息中心)

计算机科学

CSTPCD北大核心
影响因子:0.944
ISSN:1002-137X
年,卷(期):2024.51(6)
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