In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Jain:2019:10.1145/3308558.3314143,
author = {Jain, S and Bensaid, E and de, Montjoye Y-A},
doi = {10.1145/3308558.3314143},
pages = {3550--3554},
publisher = {ACM},
title = {UNVEIL: capture and visualise WiFi data leakages},
url = {http://dx.doi.org/10.1145/3308558.3314143},
year = {2019}
}
TY - CPAPER
AB - In the past few years, numerous privacy vulnerabilities have been discovered in the WiFi standards and their implementations for mobile devices. These vulnerabilities allow an attacker to collect large amounts of data on the device user, which could be used to infer sensitive information such as religion, gender, and sexual orientation. Solutions for these vulnerabilities are often hard to design and typically require many years to be widely adopted, leaving many devices at risk.In this paper, we present UNVEIL - an interactive and extendable platform to demonstrate the consequences of these attacks. The platform performs passive and active attacks on smartphones to collect and analyze data leaked through WiFi and communicate the analysis results to users through simple and interactive visualizations.The platform currently performs two attacks. First, it captures probe requests sent by nearby devices and combines them with public WiFi location databases to generate a map of locations previously visited by the device users. Second, it creates rogue access points with SSIDs of popular public WiFis (e.g. _Heathrow WiFi, Railways WiFi) and records the resulting internet traffic. This data is then analyzed and presented in a format that highlights the privacy leakage. The platform has been designed to be easily extendable to include more attacks and to be easily deployable in public spaces. We hope that UNVEIL will help raise public awareness of privacy risks of WiFi networks.
AU - Jain,S
AU - Bensaid,E
AU - de,Montjoye Y-A
DO - 10.1145/3308558.3314143
EP - 3554
PB - ACM
PY - 2019///
SP - 3550
TI - UNVEIL: capture and visualise WiFi data leakages
UR - http://dx.doi.org/10.1145/3308558.3314143
UR - http://hdl.handle.net/10044/1/70380
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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