Detecting Anomalous User Behaviors in Workflow-Driven Web Applications

Abstract

Web applications are increasingly used as portals to interact with back-end database systems and support business processes. This type of data-centric workflow-driven web application is vulnerable to two types of security threats. The first is an request integrity attack, which stems from the vulnerabilities in the implementation of business logic within web applications. The second is guideline violation, which stems from privilege misuse in scenarios where business logic and policies are too complex to be accurately defined and enforced. Both threats can lead to sequences of web requests that deviate from typical user behaviors. The objective of this paper is to detect anomalous user behaviors based on the sequence of their requests within a web session. We first decompose web sessions into workflows based on their data objects. In doing so, the detection of anomalous sessions is reduced to detection of anomalous workflows. Next, we apply a hidden Markov model (HMM) to characterize workflows on a per-object basis. In this model, the implicit business logic involved in this object defines the unobserved states of the Markov process, where the web requests are observations. To derive more robust HMMs, we extend the object-specific approach to an object-cluster approach, where objects with similar workflows are clustered and HMM models are derived on a per-cluster basis. We evaluate our models using two real systems, including an open source web application and a large web-based electronic medical record system. The results show that our approach can detect anomalous web sessions and lend evidence to suggest that the clustering approach can achieve relatively low false positive rates while maintaining its detection accuracy.