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ETD Abstract Container

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Fingerprinting Skills on Smart Speakers using Machine Learning

Naraparaju, Shriti
http://rave.ohiolink.edu/etdc/view?acc_num=ucin158400122512448

Abstract Details

2020, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
The popularity of smart speakers with virtual personal assistants such as Amazon Alexa and Google Assistant has been growing. However, the security and privacy of users using these devices have not been thoroughly examined which can lead to severe security and privacy concerns. In this thesis, we perform fingerprinting attack of skills on Amazon Echo by analyzing the encrypted network traffic and show the potential privacy leakage of users using skills on smart speakers. Skills are third-party applications that can be accessed through Amazon Alexa. Amazon Echo, the most dominant smart speaker in the current market provides the feature of Skills & Games which can be accessed through the smart speaker. For example, by enabling the BBC skill, you can hear the live streaming news from BBC on your smart speaker. Skills increase the diversity of using Amazon smart speaker by providing a variety of applications that can be used from a single platform just with the use of voice commands without accessing phone or a computer. In this fingerprinting attack, an adversary can eavesdrop on the network traffic of the smart speaker and predict the skill that is used by the user without decrypting the network traffic. For this attack, we collect the dataset consisting of 10,000 traces of network traffic related to 100 popular skills. We perform feature selection to investigate the top features that contribute towards the users privacy leakage. We implement the attack by leveraging popular machine learning models which have been used in previous network traffic analysis works. Among the different machine learning models we implement, Random Forest Classifier performs the best by achieving an accuracy of 69.85%. The experimental results show that there is a need for developers to focus on the privacy leakages of users. Our results can also be used to detect malicious attacks on smart speakers and can contribute to encrypted traffic analysis and privacy preserving at large.
Boyang Wang (Committee Chair)
Rui Dai, Ph.D. (Committee Member)
Nan Niu, Ph.D. (Committee Member)
81 p.
Computer Science
Fingerprinting; Skills

Recommended Citations

Citations

  • Naraparaju, S. (2020). Fingerprinting Skills on Smart Speakers using Machine Learning [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin158400122512448

    APA Style (7th edition)

  • Naraparaju, Shriti. Fingerprinting Skills on Smart Speakers using Machine Learning. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin158400122512448.

    MLA Style (8th edition)

  • Naraparaju, Shriti. "Fingerprinting Skills on Smart Speakers using Machine Learning." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin158400122512448

    Chicago Manual of Style (17th edition)

ucin158400122512448
444
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.