Suleiman Ahmad

Work place: Department of Cyber Security Science, Federal University of Technology, Minna, 920101, Nigeria



Research Interests: Internet of Things, Digital Forensics, Cyber Security, Cloud Computing


Mr. Ahmad Suleiman, a PhD student and faculty member of the Department of Cyber Security Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria.
Major interests: Cryptography, Cyber Security, Internet of Things Security, Digital Forensics & Investigations and Cloud Computing Security.

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

By Suleiman Ahmad John Kolo Alhassan Shafii Muhammad Abdulhamid Suleiman Zubairu

DOI:, Pub. Date: 8 Feb. 2024

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.

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