Analysis of Code Vulnerabilities in Repositories of GitHub and Rosettacode: A comparative Study

Abstract

Open-source code hosted online at programming portals is present in 99% of commercial software and is common practice among developers for rapid prototyping and cost-effective development. However, research reports the presence of vulnerabilities, which result in catastrophic security compromise, and the individual, organization, and even national secrecy are all victims of this circumstance. One of the frustrating aspects of vulnerabilities is that vulnerabilities manifest themselves in hidden ways that software developers are unaware of. One of the most critical tasks in ensuring software security is vulnerability detection, which jeopardizes core security concepts like integrity, authenticity, and availability. This study aims to explore security-related vulnerabilities in programming languages such as C, C++, and Java and present the disparities between them hosted at popular code repositories. To attain this purpose, 708 programs were examined by severity-based guidelines. A total of 1371 vulnerable codes were identified, of which 327 in C, 51 in C++, and 993 in Java. Statistical analysis also indicated a substantial difference between them, as there is ample evidence that the Kruskal-Wallis H-test p-value (.000) is below the 0.05 significance level. The Mann-Whitney Test mean rank for GitHub (Mean-rank=676.05) and Rosettacode (Mean-rank=608.64) are also different. The novelty of this article is to identify security vulnerabilities and grasp the nature severity of vulnerability in popular code repositories. This study eventually manifests a guideline for choosing a secure programming language as a successful testing technique that targets vulnerabilities more liable to breaching security.

Analyzing ML-Based IDS over Real-Traffic

“…Based on the research gap and requirements, we have worked on generating a new feasible dataset that fulfills the requirement of a researcher who wants to test their IDS on a realistic dataset. The generated dataset is publicly available for users on GitHub [50] [51]. Moreover, we have provided complete detailed methods which help analysts to generate their new dataset based on their needs and objectives.…”

Zero trust-based federated autonomous technology for code repository

“…The collaborative R&D takes the software development process as the core collaborates and integrates elements related to the R&D process, such as projects, tasks, methods, standards, tools, and data resources, providing a multi-stage, interdisciplinary and cross-departmental information collaboration environment for the R&D department, supporting the efficient and smooth development of R&D activities, shortening the R&D cycle, and ensuring R&D quality. However, the collaborative research and development mode faces security risks such as identity impersonation, illegal access, unauthorized access, and information leakage, directly threatening the security of business systems and code repositories [1]. The traditional security solution is to set up an isolated authentication area on the boundary of the enterprise for authentication and access control, divide the network with internal and external network interconnection requirements into the internal network and external network, and identify the intranet as a trusted domain.…”