Loop Categorization Analysis for Memory Leak Detection in the Linux Kernel
Rajesh Karampudi · 2023
Memory leaks in the Linux Kernel can have serious consequences, including denial of service and system instability. The dynamic allocation and customized abstractions involved in kernel memory management make it a challenging task to detect and fix memory leaks. Although existing static analysis tools have been developed to address this issue, they often struggle to handle loops efficiently. In this paper, we propose a new static analysis tool that categorizes loops to more efficiently analyze memory leak vulnerabilities in the Linux Kernel. By taking into account the complicated data flow and heavy specialization involved in kernel memory management, our tool aims to improve on existing methods. We evaluate our tool on a set of real-world case studies and demonstrate its effectiveness in identifying memory leaks. Our results show that our tool significantly outperforms existing methods in terms of detection accuracy and efficiency. Our research builds on previous work to improve memory leak analysis in the Linux Kernel and could lead to a more secure and stable operating system. Our tool represents a significant advancement in the field of memory leak detection and could have far-reaching implications for the development of reliable and secure software systems.