NDLI: Single-stroke language-agnostic keylogging using stereo-microphones and domain specific machine learning

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Single-stroke language-agnostic keylogging using stereo-microphones and domain specific machine learning

Content Provider	ACM Digital Library
Author	Sanatinia, Amirali Noubir, Guevara Narain, Sashank
Abstract	Mobile phones are equipped with an increasingly large number of precise and sophisticated sensors. This raises the risk of direct and indirect privacy breaches. In this paper, we investigate the feasibility of keystroke inference when user taps on a soft keyboard are captured by the stereoscopic microphones on an Android smartphone. We developed algorithms for sensor-signals processing and domain specific machine learning to infer key taps using a combination of stereo-microphones and gyroscopes. We implemented and evaluated the performance of our system on two popular mobile phones and a tablet: Samsung S2, Samsung Tab 8 and HTC One. Based on our experiments, and to the best of our knowledge, our system (1) is the first to exceed 90% accuracy requiring a single attempt, (2) operates on the standard Android QWERTY and number keyboards, and (3) is language agnostic. We show that stereo-microphones are a much more effective side channel as compared to the gyroscope, however, their data can be combined to boost the accuracy of prediction. While previous studies focused on larger key sizes and repetitive attempts, we show that by focusing on the specifics of the keyboard and creating machine learning models and algorithms based on keyboard areas combined with adequate filtering, we can achieve an accuracy of 90% - 94% for much smaller key sizes in a single attempt. We also demonstrate how such attacks can be instrumentalized by a malicious application to log the keystrokes of other sensitive applications. Finally, we describe some techniques to mitigate these attacks.
Starting Page	201
Ending Page	212
Page Count	12
File Format	PDF
ISBN	9781450329729
DOI	10.1145/2627393.2627417
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Privacy Sensor malware Keystroke inference using stereo-microphones Machine learning Side-channel attacks Smartphone security Tap detection with motion sensors Keystroke inference using gyroscope
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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