Author(s): Maryam Mehrnezhad, Ehsan Toreini, Siamak F. Shahandashti, Feng Hao

Download: Paper (PDF)

Date: 12 Aug 2016

Document Type: Presentations

Associated Event: NDSS Symposium 2016


In the first part of this paper, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4- digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 82.96%, which increases to 96.23% and 99.48% in the second and third attempts respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. In the second part of the paper, we study users’ perception of the risks associated with mobile phone sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our results show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.