Universal Serial Bus (USB) ports are a ubiquitous feature in computer systems and offer a cheap and efficient way to provide power and data connectivity between a host and peripheral devices. Even with the rise of cloud and off-site computing, USB has played a major role in enabling data transfer between devices. Its usage is especially prevalent in high-security environments where systems are ‘air-gapped’ and not connected to the Internet. However, recent research has demonstrated that USB is not nearly as secure as once thought, with different attacks showing that modified firmware on USB mass storage devices can compromise a host system. While many defenses have been proposed, they require user interaction, advanced hardware support (incompatible with legacy devices), or utilize device identifiers that can be subverted by an attacker. In this paper, we present Time-Print, a novel timing-based fingerprinting method, for identifying USB mass storage devices. We create a fingerprint by timing a series of read operations from different locations on a drive, as the timing variations are unique enough to identify individual USB devices. Time-Print is low overhead, completely software-based, and does not require any extra or specialized hardware. To validate the efficacy of Time-Print, we examine more than 40 USB flash drives and conduct experiments in multiple authentication scenarios. The experimental results show that Time-Print can (1) identify known/unknown brand/model USB devices with greater than 99.5% accuracy, (2) identify seen/unseen devices of the same brand/model with 95% accuracy, and (3) classify USB devices from the same brand/model with an average accuracy of 98.7%.
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Robust fingerprinting of genomic databases
Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks.
Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy.
https://github.com/xiutianxi/robust-genomic-fp-github.
- Award ID(s):
- 2141622
- NSF-PAR ID:
- 10368286
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 38
- Issue:
- Supplement_1
- ISSN:
- 1367-4803
- Format(s):
- Medium: X Size: p. i143-i152
- Size(s):
- ["p. i143-i152"]
- Sponsoring Org:
- National Science Foundation
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