research-article

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

Authors:

Abdul Rehman Javed,

Saif Ur Rehman,

Mohib Ullah Khan,

Habib Ullah KhanAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 20, Issue 5

Article No.: 87, Pages 1 - 17

https://doi.org/10.1145/3460392

Published: 30 June 2021 Publication History

Abstract

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger, an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

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Cited By

Abbas SOjo SBouazzi IAvelino Sampedro GAl Hejaili AAlmadhor AKulhánek R(2024)Securing Data From Side-Channel Attacks: A Graph Neural Network-Based Approach for Smartphone-Based Side Channel Attack DetectionIEEE Access10.1109/ACCESS.2024.346566212(138904-138920)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3465662
Ahmed AHasan MAman ASafie NIslam SAhmed FAhmed TPandey BRzayeva L(2024)Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel AttacksIEEE Access10.1109/ACCESS.2024.343121812(188435-188453)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3431218
Abbas SAlsubai SOjo SSampedro GAlmadhor AAl Hejaili ABouazzi I(2024)Active Learning for Detecting Hardware Sensors-Based Side-Channel Attack on SmartphoneArabian Journal for Science and Engineering10.1007/s13369-024-09046-xOnline publication date: 22-Apr-2024
https://doi.org/10.1007/s13369-024-09046-x
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Index Terms

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network
1. Security and privacy
  1. Security in hardware
    1. Hardware attacks and countermeasures
      1. Side-channel analysis and countermeasures

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 20, Issue 5

September 2021

320 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3467024

Editor:
Imed Zitouni
Google, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 June 2021

Accepted: 01 April 2021

Revised: 01 April 2021

Received: 01 May 2020

Published in TALLIP Volume 20, Issue 5

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Cited By

Abbas SOjo SBouazzi IAvelino Sampedro GAl Hejaili AAlmadhor AKulhánek R(2024)Securing Data From Side-Channel Attacks: A Graph Neural Network-Based Approach for Smartphone-Based Side Channel Attack DetectionIEEE Access10.1109/ACCESS.2024.346566212(138904-138920)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3465662
Ahmed AHasan MAman ASafie NIslam SAhmed FAhmed TPandey BRzayeva L(2024)Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel AttacksIEEE Access10.1109/ACCESS.2024.343121812(188435-188453)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3431218
Abbas SAlsubai SOjo SSampedro GAlmadhor AAl Hejaili ABouazzi I(2024)Active Learning for Detecting Hardware Sensors-Based Side-Channel Attack on SmartphoneArabian Journal for Science and Engineering10.1007/s13369-024-09046-xOnline publication date: 22-Apr-2024
https://doi.org/10.1007/s13369-024-09046-x
Champa ARabbi MEishita FZibran M(2024)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone SensorsSoftware Engineering and Management: Theory and Application10.1007/978-3-031-55174-1_10(139-158)Online publication date: 3-May-2024
https://doi.org/10.1007/978-3-031-55174-1_10
Maheswari RKrishnamurthy M(2024)A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder networkTransactions on Emerging Telecommunications Technologies10.1002/ett.497535:4Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4975
Muhammad ZAnwar ZJaved ASaleem BAbbas SGadekallu T(2023)Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and DefensesTechnologies10.3390/technologies1103007611:3(76)Online publication date: 12-Jun-2023
https://doi.org/10.3390/technologies11030076
Al-Kadhimi ASingh MKhalid M(2023)A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence TechniquesApplied Sciences10.3390/app1314805613:14(8056)Online publication date: 10-Jul-2023
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Prabhakar GBasel BDutta ARama Rao C(2023)Multichannel CNN-BLSTM Architecture for Speech Emotion Recognition System by Fusion of Magnitude and Phase Spectral Features Using DCCA for Consumer ApplicationsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.323697269:2(226-235)Online publication date: 1-May-2023
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Champa ARabbi MEishita FZibran M(2023)Are We Aware? An Empirical Study on the Privacy and Security Awareness of Smartphone Sensors2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA57763.2023.10197713(287-294)Online publication date: 23-May-2023
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Rehman ARazzak IXu G(2023)Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel AttacksIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.317185227:2(684-690)Online publication date: Feb-2023
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