A Survey on Acoustic Side Channel Attacks on Keyboards
- URL: http://arxiv.org/abs/2309.11012v2
- Date: Mon, 25 Sep 2023 17:23:50 GMT
- Title: A Survey on Acoustic Side Channel Attacks on Keyboards
- Authors: Alireza Taheritajar, Zahra Mahmoudpour Harris, Reza Rahaeimehr,
- Abstract summary: Mechanical keyboards are susceptible to acoustic side-channel attacks.
Researchers have developed methods that can extract typed keystrokes from ambient noise.
With the improvement of microphone technology, the potential vulnerability to acoustic side-channel attacks also increases.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Most electronic devices utilize mechanical keyboards to receive inputs, including sensitive information such as authentication credentials, personal and private data, emails, plans, etc. However, these systems are susceptible to acoustic side-channel attacks. Researchers have successfully developed methods that can extract typed keystrokes from ambient noise. As the prevalence of keyboard-based input systems continues to expand across various computing platforms, and with the improvement of microphone technology, the potential vulnerability to acoustic side-channel attacks also increases. This survey paper thoroughly reviews existing research, explaining why such attacks are feasible, the applicable threat models, and the methodologies employed to launch and enhance these attacks.
Related papers
- Acoustic Side Channel Attack on Keyboards Based on Typing Patterns [0.0]
Side-channel attacks on keyboards can bypass security measures in many systems that use keyboards as one of the input devices.
This paper proposes an applicable method that takes into account the user's typing pattern in a realistic environment.
Our method achieved an average success rate of 43% across all our case studies when considering real-world scenarios.
arXiv Detail & Related papers (2024-03-13T17:44:15Z) - OverHear: Headphone based Multi-sensor Keystroke Inference [1.9915929143641455]
We develop a keystroke inference framework that leverages both acoustic and accelerometer data from headphones.
We achieve top-5 key prediction accuracy of around 80% for mechanical keyboards and around 60% for membrane keyboards.
Results highlight the effectiveness and limitations of our approach in the context of real-world scenarios.
arXiv Detail & Related papers (2023-11-04T00:48:20Z) - A Practical Deep Learning-Based Acoustic Side Channel Attack on
Keyboards [6.230751621285321]
This paper presents a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone.
When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model.
We discuss a series of mitigation methods to protect users against these series of attacks.
arXiv Detail & Related papers (2023-08-02T10:51:36Z) - Keystroke Dynamics: Concepts, Techniques, and Applications [1.1741899892465988]
Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity.
The paper covers novel keystroke datasets, state-of-the-art keystroke authentication algorithms, keystroke authentication on touch screen and mobile devices, and various prominent applications of such techniques beyond authentication.
arXiv Detail & Related papers (2023-03-08T14:15:48Z) - Face Presentation Attack Detection [59.05779913403134]
Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment.
However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
arXiv Detail & Related papers (2022-12-07T14:51:17Z) - Deepfake audio detection by speaker verification [79.99653758293277]
We propose a new detection approach that leverages only the biometric characteristics of the speaker, with no reference to specific manipulations.
The proposed approach can be implemented based on off-the-shelf speaker verification tools.
We test several such solutions on three popular test sets, obtaining good performance, high generalization ability, and high robustness to audio impairment.
arXiv Detail & Related papers (2022-09-28T13:46:29Z) - System Fingerprint Recognition for Deepfake Audio: An Initial Dataset
and Investigation [51.06875680387692]
We present the first deepfake audio dataset for system fingerprint recognition (SFR)
We collected the dataset from the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies.
arXiv Detail & Related papers (2022-08-21T05:15:40Z) - Crack detection using tap-testing and machine learning techniques to
prevent potential rockfall incidents [68.8204255655161]
This paper proposes a system towards an automated inspection for potential rockfalls.
A robot is used to repeatedly strike or tap on the rock surface.
The sound from the tapping is collected by the robot and classified with the intent of identifying rocks that are broken and prone to fall.
arXiv Detail & Related papers (2021-10-10T22:53:36Z) - Backdoor Attack against Speaker Verification [86.43395230456339]
We show that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data.
We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
arXiv Detail & Related papers (2020-10-22T11:10:08Z) - SoK: The Faults in our ASRs: An Overview of Attacks against Automatic
Speech Recognition and Speaker Identification Systems [28.635467696564703]
We show that the end-to-end architecture of speech and speaker systems makes attacks and defenses against them substantially different than those in the image space.
We then demonstrate experimentally that attacks against these models almost universally fail to transfer.
arXiv Detail & Related papers (2020-07-13T18:52:25Z) - Backflash Light as a Security Vulnerability in Quantum Key Distribution
Systems [77.34726150561087]
We review the security vulnerabilities of quantum key distribution (QKD) systems.
We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks.
arXiv Detail & Related papers (2020-03-23T18:23:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.