Detection of Buffer Overflow Attacks with Memoization-based Rule Set

Abstract

Different abnormalities are commonly encountered in computer network systems. These types of abnormalities can lead to critical data losses or unauthorized access in the systems. Buffer overflow anomaly is a prominent issue among these abnormalities, posing a serious threat to network security. The primary objective of this study is to identify the potential risks of buffer overflow that can be caused by functions frequently used in the PHP programming language and to provide solutions to minimize these risks. Static code analyzers are used to detect security vulnerabilities, among which SonarQube stands out with its extensive library, flexible customization options, and reliability in the industry. In this context, a customized rule set aimed at automatically detecting buffer overflows has been developed on the SonarQube platform. The memoization optimization technique used while creating the customized rule set enhances the speed and efficiency of the code analysis process. As a result, the code analysis process is not repeatedly run for code snippets that have been analyzed before, significantly reducing processing time and resource utilization. In this study, a memoization-based rule set was utilized to detect critical security vulnerabilities that could lead to buffer overflow in source codes written in the PHP programming language. Thus, the analysis process is not repeatedly run for code snippets that have been analyzed before, leading to a significant reduction in processing time and resource utilization. In a case study conducted to assess the effectiveness of this method, a significant decrease in the source code analysis time was observed.