NPFTaint: Detecting highly exploitable vulnerabilities in Linux-based IoT firmware with network parsing functions

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shudan Yue , Qingbao Li , Guimin Zhang , Xiaonan Li , Bocheng Xu , Song Tian
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引用次数: 0

Abstract

The security issues of IoT firmware have become increasingly prominent, particularly taint-style vulnerabilities arising from untrusted external inputs. Although existing solutions work to detect firmware vulnerabilities automatically, they still encounter limitations regarding the accuracy of taint source identification and the efficiency of vulnerability detection. Research has shown that the network parsing function call chain, a critical path for IoT firmware to process external input data, is a high-risk area for firmware vulnerabilities. Inferring the network parsing function accurately plays a crucial role in firmware vulnerability analysis. In this paper, we propose a static analysis method called NPFTaint, which extracts the structural, behavioral, and semantic features of network parsing functions and combines supervised machine learning methods to achieve the identification of network parsing functions. Additionally, unlike traditional forward/backward analysis methods that start from classical sources or sensitive sinks, NPFTaint takes network parsing functions as the entry points, first identifying sensitive sinks on their call chains, and then using value analysis and data dependency analysis of sink-to-source to achieve the detection of highly exploitable vulnerabilities. Experimental evaluations demonstrate that NPFTaint outperforms FITS in accuracy and efficiency when identifying network parsing functions. Regarding vulnerability detection, compared to Mango, NPFTaint not only identifies taint-style vulnerabilities effectively but also improves analysis efficiency, reducing sink analysis by 40.42% and decreasing alerts by 32.77%. This solution provides a more efficient and precise vulnerability detection method for IoT firmware security, contributing to the overall security of the IoT ecosystem.
NPFTaint:通过网络解析功能检测基于linux的物联网固件中高度可利用的漏洞
物联网固件的安全问题变得越来越突出,特别是由不可信的外部输入引起的污点式漏洞。虽然现有的解决方案可以自动检测固件漏洞,但它们在识别污染源的准确性和漏洞检测的效率方面仍然受到限制。研究表明,网络解析函数调用链是物联网固件处理外部输入数据的关键路径,是固件漏洞的高风险区域。准确推断网络解析函数在固件漏洞分析中起着至关重要的作用。本文提出了一种名为NPFTaint的静态分析方法,该方法提取网络解析函数的结构、行为和语义特征,并结合监督式机器学习方法实现网络解析函数的识别。此外,与传统的前向/后向分析方法从经典源或敏感sink出发不同,NPFTaint以网络解析函数为切入点,首先识别其调用链上的敏感sink,然后通过sink-to-source的值分析和数据依赖分析,实现对高度可利用漏洞的检测。实验评估表明,NPFTaint在识别网络解析函数的准确性和效率上都优于FITS。在漏洞检测方面,与Mango相比,NPFTaint不仅能有效识别出污损型漏洞,还能提高分析效率,sink分析减少40.42%,报警减少32.77%。该方案为物联网固件安全提供了更高效、精准的漏洞检测方法,为物联网生态系统整体安全做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
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