The internal clock of the CPU uses oscillators made from quartz crystals. Small changes in these crystals can cause small but measurable differences in the clock frequency. Under a low CPU load, the function execution time distribution follows the Pareto distribution. However, the function execution time distribution no longer follows the Pareto distribution when the CPU load is high, and the CPU clock fingerprint becomes invalid. In view of this problem, this paper proposes an adaptive Pareto principle that adaptively adjusts the distribution according to the CPU load. Based on this, the robust CPU Clock Fingerprint Model based on the Adaptive Pareto Principle (CFMAP) is proposed. Via a KNN-based fingerprint recognition method, CFMAP solves the instability of existing CPU clock fingerprints under a high CPU load. Experiments show that the average recognition rate of CFMAP fingerprints is 96.82%. Moreover, they are highly robust against CPU load attacks and virtual machine attacks.

Poster: CFMAP: A Robust CPU Clock Fingerprint Model for Device Authentication / Lu, X.; Pang, R.; Lio, P.. - (2022), pp. 3407-3409. (Intervento presentato al convegno ACM Conference on Computer and Communications Security tenutosi a Los Angeles; usa) [10.1145/3548606.3563528].

Poster: CFMAP: A Robust CPU Clock Fingerprint Model for Device Authentication

Lio P.
2022

Abstract

The internal clock of the CPU uses oscillators made from quartz crystals. Small changes in these crystals can cause small but measurable differences in the clock frequency. Under a low CPU load, the function execution time distribution follows the Pareto distribution. However, the function execution time distribution no longer follows the Pareto distribution when the CPU load is high, and the CPU clock fingerprint becomes invalid. In view of this problem, this paper proposes an adaptive Pareto principle that adaptively adjusts the distribution according to the CPU load. Based on this, the robust CPU Clock Fingerprint Model based on the Adaptive Pareto Principle (CFMAP) is proposed. Via a KNN-based fingerprint recognition method, CFMAP solves the instability of existing CPU clock fingerprints under a high CPU load. Experiments show that the average recognition rate of CFMAP fingerprints is 96.82%. Moreover, they are highly robust against CPU load attacks and virtual machine attacks.
2022
ACM Conference on Computer and Communications Security
adaptive pareto principle; cpu clock fingerprint; device authentication; device fingerprint
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Poster: CFMAP: A Robust CPU Clock Fingerprint Model for Device Authentication / Lu, X.; Pang, R.; Lio, P.. - (2022), pp. 3407-3409. (Intervento presentato al convegno ACM Conference on Computer and Communications Security tenutosi a Los Angeles; usa) [10.1145/3548606.3563528].
File allegati a questo prodotto
File Dimensione Formato  
Lu_Poster_2022.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.14 MB
Formato Adobe PDF
1.14 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1728555
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact