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Received:February 17, 2023 Revised:March 14, 2023
Received:February 17, 2023 Revised:March 14, 2023
中文摘要: 操作系统内核是计算机系统中最基本的软件组件, 它控制和管理计算机硬件资源, 并提供访问和管理其他应用程序所需的接口和服务. 操作系统内核的安全性直接影响整个计算机系统的稳定性和可靠性. 内核模糊测试是一种高效、准确的安全漏洞检测方法. 然而目前内核模糊测试工作中, 存在系统调用间关系的计算开销过大且容易误判, 以及系统调用序列构造方式缺乏合理能量分配以至于很难探索低频系统调用的问题. 本文提出以N-gram模型学习系统调用间关系, 根据系统调用的出现频次信息和TF-IDF信息优先探索出现频次低或者TF-IDF值高的系统调用. 我们以极低的开销, 在Linux 4.19和5.19版本的24 h实验中分别提升了15.8%、14.7%的覆盖率. 此外, 我们挖掘到了一个已知CVE (CVE-2022-3524)、8个新崩溃, 其中一个获得了CNNVD编号(CNNVD-2023-84723975).
Abstract:The operating system kernel is the most fundamental software component in a computer system. It controls and manages computer hardware resources and provides interfaces and services necessary for accessing and managing other applications. The security of the operating system kernel directly affects the stability and reliability of the entire computer system. Kernel fuzzing is an efficient and accurate security vulnerability detection method. However, in current kernel fuzzing work, the overhead of calculating the relationship between system calls is too high, or it is easy to misjudge the relationship between system calls. In addition, the existing method for constructing system call sequences lacks reasonable energy allocation, making it difficult to explore problems of low-frequency system calls. This study proposes to learn the relationship between system calls by using an N-gram model and prioritize the expansion of system calls with low frequency or high TF-IDF values based on the frequency and TF-IDF information of system call occurrences. With minimal overhead, this study achieves a coverage increase of 15.8% and 14.7% in 24-hour experiments on Linux versions 4.19 and 5.19, respectively. Besides, one known CVE (CVE-2022-3524) and eight new crashes are discovered, one of which is numbered CNNVD (CNNVD-2023-84723975).
keywords: kernel fuzzing N-gram TF-IDF system security system call
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基金项目:国家自然科学基金(62072448)
引用文本:
张阳,范俊杰,孙晓山,张颖君,程亮.基于系统调用序列学习的内核模糊测试.计算机系统应用,2023,32(9):19-31
ZHANG Yang,FAN Jun-Jie,SUN Xiao-Shan,ZHANG Ying-Jun,CHENG Liang.Kernel Fuzzing Based on System Call Sequence Learning.COMPUTER SYSTEMS APPLICATIONS,2023,32(9):19-31
张阳,范俊杰,孙晓山,张颖君,程亮.基于系统调用序列学习的内核模糊测试.计算机系统应用,2023,32(9):19-31
ZHANG Yang,FAN Jun-Jie,SUN Xiao-Shan,ZHANG Ying-Jun,CHENG Liang.Kernel Fuzzing Based on System Call Sequence Learning.COMPUTER SYSTEMS APPLICATIONS,2023,32(9):19-31