TY - JOUR
T1 - Structural Attacks and Defenses for Flow-Based Microfluidic Biochips
AU - Baban, Navajit Singh
AU - Saha, Sohini
AU - Orozaliev, Ajymurat
AU - Kim, Jongmin
AU - Bhattacharjee, Sukanta
AU - Song, Yong Ak
AU - Karri, Ramesh
AU - Chakrabarty, Krishnendu
N1 - Publisher Copyright:
© 2007-2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Flow-based microfluidic biochips (FMBs) have seen rapid commercialization and deployment in recent years for point-of-care and clinical diagnostics. However, the outsourcing of FMB design and manufacturing makes them susceptible to susceptible to malicious physical level and intellectual property (IP)-theft attacks. This work demonstrates the first structure-based (SB) attack on representative commercial FMBs. The SB attacks maliciously decrease the heights of the FMB reaction chambers to produce false-negative results. We validate this attack experimentally using fluorescence microscopy, which showed a high correlation (R2 = 0.987) between chamber height and related fluorescence intensity of the DNA amplified by polymerase chain reaction. To detect SB attacks, we adopt two existing deep learning-based anomaly detection algorithms with ∼96% validation accuracy in recognizing such deliberately introduced microstructural anomalies. To safeguard FMBs against intellectual property (IP)-theft, we propose a novel device-level watermarking scheme for FMBs using intensity-height correlation. The countermeasures can be used to proactively safeguard FMBs against SB and IP-theft attacks in the era of global pandemics and personalized medicine.
AB - Flow-based microfluidic biochips (FMBs) have seen rapid commercialization and deployment in recent years for point-of-care and clinical diagnostics. However, the outsourcing of FMB design and manufacturing makes them susceptible to susceptible to malicious physical level and intellectual property (IP)-theft attacks. This work demonstrates the first structure-based (SB) attack on representative commercial FMBs. The SB attacks maliciously decrease the heights of the FMB reaction chambers to produce false-negative results. We validate this attack experimentally using fluorescence microscopy, which showed a high correlation (R2 = 0.987) between chamber height and related fluorescence intensity of the DNA amplified by polymerase chain reaction. To detect SB attacks, we adopt two existing deep learning-based anomaly detection algorithms with ∼96% validation accuracy in recognizing such deliberately introduced microstructural anomalies. To safeguard FMBs against intellectual property (IP)-theft, we propose a novel device-level watermarking scheme for FMBs using intensity-height correlation. The countermeasures can be used to proactively safeguard FMBs against SB and IP-theft attacks in the era of global pandemics and personalized medicine.
KW - Deep learning
KW - Microfluidic biochips
KW - structural attacks
KW - watermarking
UR - http://www.scopus.com/inward/record.url?scp=85141583579&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141583579&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2022.3220758
DO - 10.1109/TBCAS.2022.3220758
M3 - Article
C2 - 36350866
AN - SCOPUS:85141583579
SN - 1932-4545
VL - 16
SP - 1261
EP - 1275
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 6
ER -