Wireless Network Simulation to Create Machine Learning Benchmark Data

Marc Katzef, Andrew C. Cullen, Tansu Alpcan, Christopher Leckie, Justin Kopacz

Abstract: While several wireless network simulators exist, the absence of modern, standardised network datasets may adversely affect the application of machine learning methods to problems involving wireless networks. Due to the difficulty of acquiring and sharing more modern network datasets, many of the rapidly evolving techniques in machine learning are only ever ported to network analysis through archaic network datasets such as KDD'99-creating a divide between communication networks and machine learning. To address this divide, this paper presents a new network simulation framework that brings existing network and machine learning tools together to conveniently generate data consisting of PCAP or raw physical layer data and derived statistics in a format that is directly consumable by machine learning algorithms. The proposed simulation frame-work allows the user to design custom networks through a simple configuration file-based scheme instead of learning a sophisticated network simulator. Measuring our framework's performance on consumer-grade devices, raw data is efficiently generated at up to 1, 200 Ethernet frames per second, and processed into feature vectors of 5.6 samples per second (where each sample uses 1 second of simulation time) or 0.29 samples per second if physical layer signals are used.