A Web Application is a type of software application that is accessed and interacted with through a web browser over a network, typically the internet. Unlike traditional desktop applications, which are launched by your operating system, web applications must be accessed through a web browser. These applications are stored on remote servers and delivered to users over the internet, allowing users to access and use them through a web browser interface.

Web applications can serve a wide range of purposes and functionalities, from simple websites and content management systems to complex business applications. They are designed to be platform-independent, making them accessible from various devices and operating systems. Web applications commonly use a combination of server-side scripts (running on web servers) and client-side scripts (running on the user’s browser) to provide dynamic and interactive content.


Anomaly Detection on Web-User Behaviors through Deep Learning

The modern Internet has witnessed the proliferation of web applications that play a crucial role in the branding process among enterprises. Web applications provide a communication channel between potential customers and business products. However, web applications are also targeted by attackers due to sensitive information stored in these applications. Among web-related attacks, there exists a rising but more stealthy attack where attackers first access a web application on behalf of normal users based on stolen credentials. Then attackers follow a sequence of sophisticated steps to achieve the malicious purpose. Traditional security solutions fail to detect relevant abnormal behaviors once attackers login to the web application. To address this problem, we propose WebLearner, a novel system to detect abnormal web-user behaviors. As we demonstrate in the evaluation, WebLearner has an outstanding performance. In particular, it can effectively detect abnormal user behaviors with over 96% for both precision and recall rates using a reasonably small amount of normal training data.