In the domain of network management and security, detection of intrusions is a persistent challenge. Due to the large volumes of data recorded in Web server logs, analysis of them is typically forensic, taking place only after a problem has occurred. Here we describe initial steps in rendering Web log data as a sonification. The goal is to determine whether recognizable patterns can be detected, either in real time, or as an after-the-fact analysis. A combination of Python and SuperCollider  are used to parse and sonify the data. Results of this work will become part of a collective pool of methodologies used in ongoing data rendering experiments carried out by Penn State's Center for Network Centric Cognition and Information Fusion (NC2IF) .