BlackWatch：提高Web应用程序的攻击感知能力
BlackWatch: Increasing Attack Awareness Within Web Applications
摘要：
许多人依赖Web应用程序提供的服务。应用程序必须实施适当的安全措施以防止安全事件。目前，Web应用程序将资源集中在安全的预防方面。虽然预防是安全过程的重要组成部分，但开发人员还必须在其Web应用程序中实现一定程度的攻击感知。能够检测攻击何时发生，为应用程序提供了针对恶意用户执行响应的能力，以减缓或阻止他们的攻击。这项研究旨在通过使用我们的工具BlackWatch识别Web应用程序上下文中的恶意行为来提高Web应用程序的安全性。该工具是一个基于Python的应用程序，它分析客户端Web应用程序中发生的可疑事件，目的是识别恶意行为模式。根据初步研究的结果，BlackWatch可以有效地检测来自身份验证和未经身份验证的用户的攻击。此外，开发人员的用户测试表明，BlackWatch用户友好，易于集成到现有的应用程序中。未来的工作旨在进一步开发BlackWatch解决方案以供公开发布。
英文摘要：
Web applications are relied upon by many for the services they provide. It is essential that applications implement appropriate security measures to prevent security incidents. Currently, web applications focus resources towards the preventative side of security. Whilst prevention is an essential part of the security process, developers must also implement a level of attack awareness into their web applications. Being able to detect when an attack is occurring provides applications with the ability to execute responses against malicious users in an attempt to slow down or deter their attacks. This research seeks to improve web application security by identifying malicious behaviour from within the context of web applications using our tool BlackWatch. The tool is a Python-based application which analyses suspicious events occurring within client web applications, with the objective of identifying malicious patterns of behaviour. Based on the results from a preliminary study, BlackWatch was effective at detecting attacks from both authenticated, and unauthenticated users. Furthermore, user tests with developers indicated BlackWatch was user friendly, and was easy to integrate into existing applications. Future work seeks to develop the BlackWatch solution further for public release.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
