The design of Datascapes: toward a design framework for sonification for anomaly detection in AI-supported networked environments

Abstract

There is a growing need for solutions that can improve the communication between anomaly detection algorithms and human operators. In the context of real-time monitoring of networked systems, it is crucial that new solutions do not increase the burden on an already overloaded visual channel. Sonification can be leveraged as a peripheral monitoring tool that complements current visualization systems. We conceptualized, designed, and prototyped Datascapes, a framework project that explores the potential of sound-based applications for the monitoring of cyber-attacks on AI-supported networked environments. Within Datascapes, two Design Actions were realized that applied sonification on the monitoring and detection of anomalies in (1) water distribution networks and (2) Internet networks. Two series of prototypes were implemented and evaluated in a real-world environment with eight experts in network management and cybersecurity. This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network. Furthermore, we define and present a design methodology and evaluation protocol that, albeit grounded in sonification for anomaly detection, can support designers in the definition, development, and validation of real-world sonification applications.