The IT Sorcerer’s Apprentice!
Machine learning seemed an odd fit at first. Our company was formed as a simple network discovery tool, as reliable and useful as a carpenter’s hammer. If you don’t know us at Who’s On My WiFi, we started off by offering a platform-agnostic ARP scanning tool to discover connected devices on a network over time. Our company path changed drastically when we started saving all the information we were scanning. We made an important discovery: WWW.WhoIsOnMyWiFi.com
Large amounts of network data is useless without some way to make sense of all that information!
For example, the first problem people tried to solve using our software was detecting if an unknown device suddenly joined the network.
We initially required that customers tag devices as KNOWN, and then they could be alerted to any UNKNOWN devices. But there is a problem with this, especially on larger networks. Tagging devices is time-consuming and requires constant updating to be useful. Our customers’ IT managers would be tasked with tagging staff and network devices, while reporting on guests that entered their building. It was an up-front workload compounded by the inevitable influx of new devices or [shudders] network equipment overhauls.
The next problem people started solving with our basic network detection was trying to determine the number of people using a public WiFi network over time. Although this sounds simple, to get accurate usage patterns, again, there is an up front cost of going through and tagging all equipment that could possibly be on a public WiFi network to exclude it. Otherwise, always on devices like network equipment or printers incorrectly impacted the results. And what about employees using the public WiFi? Should they be counted as visitors or not?
To painstakingly go through a large public venue, tag all switches, APs, as well as employee equipment and smartphones was too much maintenance for IT administrators to keep up with.
Enter Machine Learning.