With the Wigle WiFi app running on an Android phone, a hacker can discover and map any nearby network, including those created by printers and other insecure devices. The default tools to analyze the resulting data can fall short of what a hacker needs, but by importing wardriving data into Jupyter Notebook, we can map all Wi-Fi devices we encounter and slice through the data with ease.
Thanks to low-cost Android smartphones equipped with GPS and Wi-Fi sensors, wardriving has gotten easier than ever. With a $60 Android smartphone and Wigle WiFi, it's possible to map the time and location that you encountered any Wi-Fi or Bluetooth device, with cellular data towers thrown in for good measure.
The data produced by wardriving can be extremely valuable. Still, the tools to analyze that data automatically can also come with the problem of exposing the networks you collected by publishing them to a public database like Wigle.net.
In [ ]: import pandas as pd import folium # (https://pypi.python.org/pypi/folium) df = pd.read_csv('/Users/skickar/Downloads/WigleWifi_20190723192904.csv', delimiter = ',', encoding='latin-1', header=1) mymap = folium.Map( location=[ df.CurrentLatitude.mean(), df.CurrentLongitude.mean() ], zoom_start=12) #folium.PolyLine(df[['Latitude','Longitude']].values, color="red", weight=2.5, opacity=1).add_to(mymap) for coord in df[[ ..
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