CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Apr 20, 2026

QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection

arXiv Security Archived Apr 20, 2026 ✓ Full text saved

arXiv:2604.15845v1 Announce Type: new Abstract: Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device (HID) emulation attacks, such as those enabled by the USB Rubber Ducky, exploit this assumption to inject arbitrary keystroke sequences while bypassing traditional defenses. Existing countermeasures rely on simple heuristics based on typing speed or timing regularity, which can be easily evaded

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 17 Apr 2026] QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection Alessandro Lotto, Francesco Marchiori, Mauro Conti Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device (HID) emulation attacks, such as those enabled by the USB Rubber Ducky, exploit this assumption to inject arbitrary keystroke sequences while bypassing traditional defenses. Existing countermeasures rely on simple heuristics based on typing speed or timing regularity, which can be easily evaded through basic randomization. Keystroke dynamics analysis offers a more robust alternative by modeling temporal typing behavior. However, prior work frames this problem as behavioral authentication, verifying whether input originates from a specific user rather than detecting automated injection. An alternative approach is continuous monitoring via keylogging integrated with intrusion detection systems, but this requires access to input content, raising significant privacy concerns. In this paper, we provide the first systematic characterization of keystroke dynamics for human-vs-machine discrimination, independent of user identity. Guided by five research questions, we show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features, eliminating the need for content access or user profiling. Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion. Instead, robustness depends on exposure to structurally diverse generation strategies rather than increased model complexity. Finally, we quantify the trade-off between detection timeliness and reliability across varying keystroke sequence lengths, identifying practical operating points for early and effective attack interception. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.15845 [cs.CR]   (or arXiv:2604.15845v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.15845 Focus to learn more Submission history From: Alessandro Lotto [view email] [v1] Fri, 17 Apr 2026 08:53:24 UTC (1,094 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 20, 2026
    Archived
    Apr 20, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗