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Web applications in widespread use have always been the target of large-scaleattacks, leading to massive disruption of services and financial loss, as inthe Equifax data breach. It has become common practice to deploy webapplication in containers like Docker for better portability and ease ofdeployment. We design a system called AppMine for lightweight monitoring of webapplications running in Docker containers and detection of unknown webvulnerabilities. AppMine is an unsupervised learning system, trained only onlegitimate workloads of web application, to detect anomalies based on eithertraditional models (PCA and one-class SVM), or more advanced neural-networkarchitectures (LSTM). In our evaluation, we demonstrate that the neural networkmodel outperforms more traditional methods on a range of web applications andrecreated exploits. For instance, AppMine achieves average AUC scores as highas 0.97 for the Apache Struts application (with the CVE-2017-5638 exploit usedin the Equifax breach), while the AUC scores for PCA and one-class SVM are 0.81and 0.83, respectively.

AppMine: Behavioral Analytics for Web Application Vulnerability Detection