fuzzing

简明释义

英[ˈfʌzɪŋ]美[ˈfʌzɪŋ]

n. 起毛；绒毛面

v. 变成绒毛状（fuzz 的 ing 形式）

英英释义

单词用法

同义词

反义词

例句

1.The results show that the poor anti - fuzzing and anti-pilling performance of milk protein knitted fabric is due to good wear resistance performance, which cause pills drop slowly.

结果表明牛奶蛋白纤维针织物干磨后抗起毛起球性能差的主要原因是纤维比较耐磨，落球较慢。

2.Of course, if the threat is that a rogue employee of the software company might produce a malicious patch, fuzzing becomes important again.

当然，如果威胁一个软件的流氓雇员公司可能会产生恶意修补程序，起毛变得重要的了。

3.In instant messenger and E-mail conversations, Wu explains how he USES a method known as "fuzzing" to harvest those bugs.

在和Wu Shi的实时通讯和e - mail交流中，他对我们解析了他是如何利用一种被称为“fuzzing(侦察)”的方法去发现那些漏洞的。

4.How to use Wireshark to help to create a protocol fuzzing framework?

如何使用Wireshark帮助创造一个协议模糊框架？

5.The simplest thing you can do to protect against fuzzing is add a checksum to your data.

能够保护程序抵御模糊攻击的最简单的方法是将一个检验和添加到数据中。

6.To determine the pilling and fuzzing characteristics of textile fabrics. chemical fiber, blended, knitting, woven fabrics.

用于测试毛织物和化纤纯纺、混纺、针织、机织物的起毛起球状况。

7.For XML, try fuzzing individual element content and attribute values rather than selecting a random section of the bytes in the document to replace.

对于XML，试着模糊单独的元素内容和属性值，而不是从文档中挑选一部分随机的字节进行替换。

8.Raising machine is one kind of equipment of textile post - disposing, also named as fluffing machine or fuzzing machine.

起毛机又称为拉绒机、起绒机，是一种织物后处理设备。

9.The security team is conducting fuzzing to identify vulnerabilities in the application.

安全团队正在进行模糊测试以识别应用程序中的漏洞。

10.By using fuzzing, developers can simulate unexpected inputs to test software stability.

通过使用模糊测试，开发人员可以模拟意外输入来测试软件的稳定性。

11.Automated fuzzing tools help find bugs that manual testing might miss.

自动化的模糊测试工具有助于发现手动测试可能遗漏的错误。

12.The fuzzing process revealed several critical security flaws in the code.

模糊测试过程揭示了代码中的几个关键安全缺陷。

13.Many companies now implement fuzzing as part of their software development lifecycle.

许多公司现在将模糊测试作为其软件开发生命周期的一部分。

作文

In the realm of software testing, one technique that has gained significant attention is fuzzing. This method involves providing invalid, unexpected, or random data as inputs to a computer program. The primary goal of fuzzing is to discover vulnerabilities and bugs that could be exploited by malicious users. By subjecting software to a wide range of inputs, developers can identify how their programs behave under stress and whether they can handle unexpected situations gracefully. The concept of fuzzing originated in the late 1980s when researchers were looking for ways to improve software reliability. Since then, it has evolved into a sophisticated testing methodology used by security professionals and developers alike. The process typically involves a fuzzer, which is a tool designed to generate test cases automatically. These test cases can vary greatly, from simple malformed inputs to complex data structures that mimic real-world scenarios. One of the key advantages of fuzzing is its ability to uncover issues that traditional testing methods may miss. For instance, while manual testing often relies on predefined inputs and expected outcomes, fuzzing introduces randomness, allowing for a broader exploration of potential edge cases. This randomness can lead to the discovery of critical vulnerabilities, such as buffer overflows, memory leaks, and unhandled exceptions. Moreover, fuzzing is particularly effective for testing applications that process user-generated content, such as web browsers and file parsers. These applications often encounter unpredictable data from various sources, making them susceptible to attacks. By using fuzzing, developers can simulate these scenarios and ensure their software can withstand malicious inputs. However, it is important to note that fuzzing is not a silver bullet in software testing. While it can significantly enhance the robustness of an application, it should be used in conjunction with other testing techniques. Static analysis, dynamic analysis, and manual testing all play essential roles in creating a comprehensive testing strategy. Each approach has its strengths and weaknesses, and together they provide a more complete picture of an application's security posture. In practice, implementing fuzzing requires careful planning and execution. Developers must define the scope of their testing and select appropriate tools that fit their specific needs. There are many fuzzing tools available today, ranging from open-source options to commercial products. Some popular examples include AFL (American Fuzzy Lop), LibFuzzer, and Honggfuzz. Each tool has unique features and capabilities, so choosing the right one can significantly impact the effectiveness of the fuzzing process. In conclusion, fuzzing is a powerful technique that plays a crucial role in modern software development and security testing. By generating unexpected inputs and observing how software responds, developers can identify and rectify vulnerabilities before they can be exploited. As technology continues to evolve, the importance of fuzzing will only grow, making it an essential practice for anyone involved in software development and security. Embracing this technique can lead to more secure applications and a safer digital environment for all users.

在软件测试领域，一种备受关注的技术是模糊测试。这种方法涉及向计算机程序提供无效、意外或随机的数据作为输入。模糊测试的主要目标是发现可能被恶意用户利用的漏洞和缺陷。通过对软件施加各种输入，开发人员可以识别他们的程序在压力下的表现，以及它们是否能够优雅地处理意外情况。 模糊测试的概念起源于1980年代末，当时研究人员正在寻找改善软件可靠性的方法。从那时起，它已演变为一种复杂的测试方法论，被安全专业人士和开发人员广泛使用。这个过程通常涉及一个模糊器，这是一个旨在自动生成测试用例的工具。这些测试用例可以有很大差异，从简单的格式错误输入到复杂的数据结构，这些数据结构模拟现实世界的场景。 模糊测试的一个关键优势是它能够发现传统测试方法可能遗漏的问题。例如，尽管手动测试通常依赖于预定义的输入和预期结果，但模糊测试引入了随机性，允许更广泛地探索潜在的边缘案例。这种随机性可以导致发现关键的漏洞，如缓冲区溢出、内存泄漏和未处理的异常。 此外，模糊测试对于测试处理用户生成内容的应用程序特别有效，例如网页浏览器和文件解析器。这些应用程序通常会遇到来自各种来源的不可预测数据，使其容易受到攻击。通过使用模糊测试，开发人员可以模拟这些场景，并确保他们的软件能够抵御恶意输入。 然而，需要注意的是，模糊测试并不是软件测试的灵丹妙药。虽然它可以显著增强应用程序的稳健性，但它应该与其他测试技术结合使用。静态分析、动态分析和手动测试在创建全面的测试策略中都扮演着重要角色。每种方法都有其优缺点，结合在一起可以提供更全面的应用程序安全状况。 在实践中，实施模糊测试需要仔细的规划和执行。开发人员必须定义测试的范围，并选择适合其特定需求的合适工具。如今，有许多可用的模糊测试工具，从开源选项到商业产品。一些流行的例子包括AFL（美国模糊测试）、LibFuzzer和Honggfuzz。每种工具都有独特的功能和能力，因此选择正确的工具可以显著影响模糊测试过程的有效性。 总之，模糊测试是一种强大的技术，在现代软件开发和安全测试中发挥着至关重要的作用。通过生成意外输入并观察软件的响应，开发人员可以在漏洞被利用之前识别和修复它们。随着技术的不断发展，模糊测试的重要性只会增加，使其成为任何参与软件开发和安全工作的人必不可少的实践。接受这一技术可以导致更安全的应用程序，为所有用户创造一个更安全的数字环境。