Keystroke Dynamics: Concepts, Techniques, and Applications
- URL: http://arxiv.org/abs/2303.04605v2
- Date: Sun, 23 Jun 2024 07:40:30 GMT
- Title: Keystroke Dynamics: Concepts, Techniques, and Applications
- Authors: Rashik Shadman, Ahmed Anu Wahab, Michael Manno, Matthew Lukaszewski, Daqing Hou, Faraz Hussain,
- Abstract summary: 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.
- Score: 1.1741899892465988
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques such as biometric authentication systems have been devised in recent years. This paper surveys keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity as it promises to be non-intrusive and cost-effective. Also, no additional hardware is required, making it convenient to deploy. This survey 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. The paper covers all the significant aspects of keystroke dynamics and can be considered as a reference for future researchers in this domain. The paper includes a discussion of the latest keystroke datasets, providing researchers with up-to-date resources for analysis and experimentation. Additionally, we review the state-of-the-art algorithms adopted within this domain, offering insights into the cutting-edge techniques utilized for keystroke analysis. Moreover, our paper explains the diverse applications of keystroke dynamics, particularly focusing on security, verification and identification uses. Beyond these crucial areas, we mention other additional applications where keystroke dynamics can be applied, broadening the scope of understanding regarding its potential impact across various domains.
Related papers
- Evaluation Scheme to Analyze Keystroke Dynamics Methods [0.0]
In this paper, we introduce requirements for biometric authentication and keystroke dynamics.
Results indicate that keystroke dynamics can be used as another authentication method but can be bypassed by stronger adversaries.
arXiv Detail & Related papers (2024-07-23T07:35:33Z) - Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions.
This survey comprehensively reviews the latest developments in deepfake generation and detection.
We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
arXiv Detail & Related papers (2024-03-26T17:12:34Z) - Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark
Evaluation [21.63351064421652]
Keystroke dynamics (KD) for biometric verification has several advantages.
KD is among the most discriminative behavioral traits.
We present a new experimental framework to benchmark KD-based biometric verification performance and fairness.
arXiv Detail & Related papers (2023-11-10T11:23:28Z) - A Comprehensive Survey on Applications of Transformers for Deep Learning
Tasks [60.38369406877899]
Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data.
transformer models excel in handling long dependencies between input sequence elements and enable parallel processing.
Our survey encompasses the identification of the top five application domains for transformer-based models.
arXiv Detail & Related papers (2023-06-11T23:13:51Z) - Conditional Generative Adversarial Network for keystroke presentation
attack [0.0]
We propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system.
Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user.
Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.
arXiv Detail & Related papers (2022-12-16T12:45:16Z) - Mobile Keystroke Biometrics Using Transformers [11.562974686156196]
This paper focuses on improving keystroke biometric systems on the free-text scenario.
Deep learning methods have been proposed in the literature, outperforming traditional machine learning methods.
To the best of our knowledge, this is the first study that proposes keystroke biometric systems based on Transformers.
arXiv Detail & Related papers (2022-07-15T16:50:11Z) - Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
We instrument each machine by means of an external agent to generate user activity.
This agent combines both deterministic and deep learning based methods to adapt to different environment.
We also propose conditional text generation models to facilitate the creation of conversations and documents.
arXiv Detail & Related papers (2021-11-23T10:53:29Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - Machine Learning-Based Analysis of Free-Text Keystroke Dynamics [7.447152998809457]
Keystroke dynamics can be used to analyze the way that a user types based on various keyboard input.
Previous work has shown that user authentication and classification can be achieved based on keystroke dynamics.
We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU)
arXiv Detail & Related papers (2021-07-01T14:50:17Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z)
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.