Hardware-Rooted Tri-Factor Authentication for Kernel-Enforced Ransomware Protection: TPM Attestation, Device Fingerprinting, and Post-Quantum USB Tokens

Ransomware that exploits legitimate administrative credentials exposes a fundamental weakness in conventional endpoint protection: once an attacker holds SYSTEM-level privileges, user-mode security software can be terminated or bypassed because it operates at the same privilege level as the malware itself. This paper addresses that structural problem through a hardware-rooted tri-factor authentication architecture enforced at Ring 0 by a Windows kernel minifilter driver. Credential theft alone becomes insufficient for file access because three independent hardware factors must all be satisfied: TPM 2.0 PCR binding ties token validity to a specific boot state, multi-dimensional device fingerprinting binds it to the physical machine, and post-quantum USB token signing using Dilithium3 (ML-DSA-65, FIPS 204) binds it to a physical hardware token. These factors are composed into a layered cryptographic token; any write operation that cannot present a valid token is blocked before it reaches the NTFS layer. The system incorporates a five-level security degradation model so that protection continuity is maintained even when hardware factors are partially unavailable. Empirical evaluation across two platforms shows full tri-factor token issuance completing in 111.2 ms ± 15.7 ms under software TPM fallback, with ML-DSA-65 signing taking 0.92 ms ± 0.96 ms including USB I/O overhead. Hamming distance analysis between the two test machines yields 263 of 512 bits differing (51.4%), confirming fingerprint distinctiveness across hardware. Six attack scenarios are evaluated including credential-abuse paths, alongside MITRE ATT&CK coverage analysis for WannaCry, LockBit 3.0, BlackCat, and Ryuk. Two open validation gaps are acknowledged: fleet-scale FAR/FRR evaluation across a broader hardware population, and testing against live ransomware samples. Source code is publicly available on GitHub.