# Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs

arXiv: https://arxiv.org/abs/2406.15335 · HTML: https://mlsanity.com/paper/2406.15335/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text](https://mlsanity.com/paper/2212.12672/) (2022)
- [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://mlsanity.com/paper/2306.07799/) (2023)
- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning](https://mlsanity.com/paper/2306.01761/) (2023)
- [Fast Free-text Authentication via Instance-based Keystroke Dynamics](https://mlsanity.com/paper/2006.09337/) (2020)
- [Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic](https://mlsanity.com/paper/2005.07688/) (2020)
- [E-cheating Prevention Measures: Detection of Cheating at Online Examinations Using Deep Learning Approach -- A Case Study](https://mlsanity.com/paper/2101.09841/) (2021)
- [Spotting Fake Profiles in Social Networks via Keystroke Dynamics](https://mlsanity.com/paper/2311.06903/) (2023)
- [On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment](https://mlsanity.com/paper/2006.09501/) (2020)

## Built on by (most cited first, 6 of 6)

- [LLM-Assisted Cheating Detection in Korean Language via Keystrokes](https://mlsanity.com/paper/2507.22956/) (2025)
- [On the Insecurity of Keystroke-Based AI Authorship Detection: Timing-Forgery Attacks Against Motor-Signal Verification](https://mlsanity.com/paper/2601.17280/) (2026)
- [Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs](https://mlsanity.com/paper/2507.16860/) (2025)
- [Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments](https://mlsanity.com/paper/2601.11093/) (2026)
- [Does AI Assistance Leave a Temporal Fingerprint? Detecting Overreliance in AI-Assisted Writing and Programming](https://mlsanity.com/paper/2609.17883/) (2026)
- [Detecting GPT-Assisted Writing Using Interpretable Stylometric Features](https://mlsanity.com/paper/2609.26687/) (2026)

## Abstract (arXiv)

The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and from 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine and assisted writing. The outcomes of this study enhance our understanding of how users interact with generative AI and have implications for improving the reliability of digital educational platforms.