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A hybrid approach to improving program security

机译：一种提高程序安全性的混合方法

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摘要

The security of computer programs and systems is a very critical issue. With the number of attacks launched on computer networks and software, businesses and IT professionals are taking steps to ensure that their information systems are as secure as possible. However, many programmers do not think about adding security to their programs until their projects are near completion. This is a major mistake because a system is as secure as its weakest link. If security is viewed as an afterthought, it is highly likely that the resulting system will have a large number of vulnerabilities, which could be exploited by attackers. One of the reasons programmers overlook adding security to their code is because it is viewed as a complicated or time-consuming process. This paper presents a tool that will help programmers think more about security and add security tactics to their code with ease. We created a model that learns from existing open source projects and documentation using machine learning and text mining techniques. Our tool contains a module that runs in the background to analyze code as the programmer types and offers suggestions of where security could be included. In addition, our tool fetches existing open source implementations of cryptographic algorithms and sample code from repositories to aid programmers in adding security easily to their projects.
机译：计算机程序和系统的安全性是一个非常关键的问题。随着在计算机网络和软件上发起的攻击数量不断增加，企业和IT专业人员正在采取步骤来确保其信息系统尽可能安全。但是，许多程序员直到他们的项目接近完成时才考虑为他们的程序增加安全性。这是一个重大错误，因为系统与其最薄弱的环节一样安全。如果将安全性视为事后考虑，则最终系统很有可能会存在大量漏洞，攻击者可能会利用这些漏洞。程序员忽略为代码添加安全性的原因之一是因为它被视为复杂或耗时的过程。本文提出了一种工具，可以帮助程序员更多地考虑安全性，并轻松地在其代码中添加安全策略。我们创建了一个模型，该模型使用机器学习和文本挖掘技术从现有的开源项目和文档中学习。我们的工具包含一个在后台运行的模块，用于在程序员键入代码时分析代码，并提供有关可以在何处包含安全性的建议。此外，我们的工具从存储库中获取加密算法和示例代码的现有开源实现，以帮助程序员轻松地为其项目添加安全性。

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