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. While 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 behavior from within the context of web applications using our tool BlackWatch. The tool is a Python-based application which analyzes suspicious events occurring within client web applications, with the objective of identifying malicious patterns of behavior. This approach avoids issues typically encountered with traditional web application firewalls. 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.
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