Enhancing Fast Fourier Transform Algorithm for Keystroke Acoustic Emanation Denoising Strategy on Real-Time Scenario

Full Text (PDF, 497KB), PP.16-23

Views: 0 Downloads: 0

Author(s)

Suleiman Ahmad 1,* John Kolo Alhassan 2 Shafii Muhammad Abdulhamid 1 Suleiman Zubairu 3

1. Department of Cyber Security Science, Federal University of Technology, Minna, 920101, Nigeria

2. Department of Computer Science, Federal University of Technology, Minna, 920101, Nigeria

3. Department of Telecommunication Engineering, Federal University of Technology, Minna, 920101, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2024.01.02

Received: 14 May 2023 / Revised: 11 Jun. 2023 / Accepted: 22 Aug. 2023 / Published: 8 Feb. 2024

Index Terms

Enhanced Fast Fourier Transform, Keystrokes, Smartphones, Denoising, Real-Time Environment

Abstract

The use of virtual keyboards in mobile devices such as smartphones and tablets has become an essential tool for inputting information. The sound of keystrokes has been observed in previous studies to be recorded along with ambient noises, such as those produced by uncontrolled student noise, fans, doors and windows, moving cars, and similar sources. The presence of such noises negatively affects the quality of the keystrokes signal, which in turn affects keystroke analysis. The traditional FFT-based denoising methods are vital but they are often limited by their inability to adapt to the varying characteristics of real-world audio and noises. This paper proposes an enhanced Fast Fourier Transform (FFT) with an adaptive threshold technique that reduces ambient noises. The adaptive threshold technique is developed to identify frequency bins that contain noise and set their sizes to zero or attenuate them to reduce the noise. The paper evaluates the performance of the enhanced FFT with adaptive threshold on keystrokes recorded audio and validates it through extensive experimentation. The results show that the enhanced FFT outperforms the traditional FFT in terms of speed and the amount of noise removed from the recorded audio signal, indicating a significant improvement.

Cite This Paper

Suleiman Ahmad, John Kolo Alhassan, Shafi’I Muhammad Abdulhamid, Suleiman Zubair, "Enhancing Fast Fourier Transform Algorithm for Keystroke Acoustic Emanation Denoising Strategy on Real-Time Scenario", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.1, pp. 16-23, 2024. DOI:10.5815/ijem.2024.01.02

Reference

[1]I. Shumailov, L. Simon, J. Yan, and R. Anderson, “Hearing your touch: A new acoustic side channel on smartphones,” pp. 1–23, 2019.
[2]D. Asonov and R. Agrawal, “Keyboard acoustic emanations,” Proc. - IEEE Symp. Secur. Priv., vol. 2004, no. 1, pp. 3–11, 2004.
[3]L. Zhuang, F. Zhou, and J. D. Tygar, “Keyboard Acoustic Emanations Revisited,” pp. 373–382, 2005.
[4]T. Halevi and N. Saxena, “Keyboard acoustic side channel attacks: exploring realistic and security-sensitive scenarios,” Int. J. Inf. Secur., vol. 14, no. 5, pp. 443–456, 2015.
[5]A. Yeredor and R. Aviv, “Dictionary attacks using keyboard acoustic emanations,” no. JANUARY 2006, 2016.
[6]A. Zarandy, I. Shumailov, R. Anderson, and A. Alexa, “D ECODING SMARTPHONE SOUNDS WITH A VOICE ASSISTANT,” 2020.
[7]G. De Souza, F. Hae, and Y. Kim, “Differential audio analysis : a new side-channel attack on PIN pads,” Int. J. Inf. Secur., 2018.
[8]D. Slater, S. Novotney, and J. Moore, “Robust Keystroke Transcription from the Acoustic Side-Channel,” in In 2019 Annual Computer Security Applications Conference (ACSAC ’19), 2019, pp. 776–787.
[9]A. M. A. Zaw Soe Yi, “Performance Comparison of Noise Detection and Elimination Methods For Audio Signals,” vol. 03, no. 14, pp. 3069–3073, 2014.
[10]A. Abuzneid, M. Uddin, S. A. Naz, and O. Abuzaghleh, “An Algorithm to Remove Noise from Audio Signal by Noise Subtraction,” pp. 5–10, 2008.
[11]S. Lee and H. Kwon, “applied sciences A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection,” pp. 1–24, 2020.
[12]H. Kim, B. Joe, and Y. Liu, “TapSnoop : Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices,” 2020.
[13]D. N. H. Thanh, “A Review on CT and X-Ray Images Denoising Methods Image formation in medical imag- ing systems and Poisson noise,” vol. 43, pp. 151–159, 2019.
[14]Y. Hu, “Time-Frequency Analysis , Denoising , Compression , Segmentation , and Classification of PCG Signals,” vol. 8, 2020.
[15]B. Goyal, A. Dogra, S. Agrawal, and B. S. Sohi, “Noise Issues Prevailing in Various Types of Medical Images,” vol. 11, no. September, pp. 1227–1237, 2018.
[16]Y. Li and L. Wang, “A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise , minimum mean square variance criterion and least mean square adaptive fi lter,” Def. Technol., no. xxxx, 2019.
[17]H. Abdelnasser, “MagStroke : A Magnetic Based Virtual Keyboard for Off-the-Shelf Smart Devices,” 2020.
[18]B. Nassi, Y. Pirutin, T. Galor, Y. Elovici, and B. Zadov, “Glowworm Attack : Optical TEMPEST Sound Recovery via a Device ’ s Power Indicator LED,” no. 3, 2021.
[19]N. Zhang, Z. Nie, Y. Luo, L. Du, X. Wang, and L. Wang, “A Reconfigurable Overlapping FFT / IFFT Filter for ECG Signal De-noising,” 2014 IEEE Int. Symp. Bioelectron. Bioinforma. (IEEE ISBB 2014), no. April 2014, pp. 1–4, 2020.
[20]P. Cheng and I. Ethem, “SonarSnoop : active acoustic side-channel attacks,” Int. J. Inf. Secur., vol. 19, no. 2, pp. 213–228, 2020.
[21]B. Nassi et al., “Lamphone : Passive Sound Recovery from a Desk Lamp ’ s Light Bulb Vibrations,” 2022.