Structural Attacks and Defenses for Flow-Based Microfluidic Biochips

Navajit Singh Baban, Sohini Saha, Ajymurat Orozaliev, Jongmin Kim, Sukanta Bhattacharjee, Yong Ak Song, Ramesh Karri, Krishnendu Chakrabarty

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1261-1275
Number of pages15
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number6
StatePublished - Dec 1 2022


  • Deep learning
  • Microfluidic biochips
  • structural attacks
  • watermarking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering


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