Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
About 75% of software security incidents are caused by software vulnerability. In addition, the after-market repairing cost of the software is higher by more than 30 times than that in the design stage. In this background, the secure coding has been proposed as one of the ways to solve this kind of maintenance problems. Various institutions have addressed the weakness patterns of the standard software. A new Korean programming language Saesark has been proposed to resolve the security weakness on the language level. However, the previous study on Saesark can not resolve the security weakness caused by the API. This paper proposes a way to resolve the security weakness due to the API. It adopts a static analyzer inspecting dangerous methods. It classifies the dangerous methods of the API into two groups: the methods of using tainted data and those accepting in-flowing tainted data. It analyses the security weakness in four steps: searching for the dangerous methods, configuring a call graph, navigating a path between the method for in-flowing tainted data and that uses tainted data on the call graph, and reporting the security weakness detected. To measure the effectiveness of this method, two experiments have been performed on the new version of Saesark adopting the static analysis. The first experiment is the comparison of it with the previous version of Saesark according to the Java Secure Coding Guide. The second experiment is the comparison of the improved Saesark with FindBugs, a Java program vulnerability analysis tool. According to the result, the improved Saesark is 15% more safe than the previous version of Saesark and the F-measure of it 68%, which shows the improvement of 9% point compared to 59%, that of FindBugs.
About 75% of software security incidents are caused by software vulnerability. In addition, the after-market repairing cost of the software is higher by more than 30 times than that in the design stage. In this background, the secure coding has been proposed as one of the ways to solve this kind of maintenance problems. Various institutions have addressed the weakness patterns of the standard software. A new Korean programming language Saesark has been proposed to resolve the security weakness on the language level. However, the previous study on Saesark can not resolve the security weakness caused by the API. This paper proposes a way to resolve the security weakness due to the API. It adopts a static analyzer inspecting dangerous methods. It classifies the dangerous methods of the API into two groups: the methods of using tainted data and those accepting in-flowing tainted data. It analyses the security weakness in four steps: searching for the dangerous methods, configuring a call graph, navigating a path between the method for in-flowing tainted data and that uses tainted data on the call graph, and reporting the security weakness detected. To measure the effectiveness of this method, two experiments have been performed on the new version of Saesark adopting the static analysis. The first experiment is the comparison of it with the previous version of Saesark according to the Java Secure Coding Guide. The second experiment is the comparison of the improved Saesark with FindBugs, a Java program vulnerability analysis tool. According to the result, the improved Saesark is 15% more safe than the previous version of Saesark and the F-measure of it 68%, which shows the improvement of 9% point compared to 59%, that of FindBugs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.