Anomaly Detection Method based on Discrete Particle Swarm Optimization for Continuous-Flow Microfluidic Biochips

Continuous-Flow Microfluidic Biochips (CFMBs) have been widely applied in various biochemical fields due to their capabilities of precise control, high integration, and automation. However, insecure supply chains enable malicious actors to tamper with biochips, compromising their integrity and leading to failed bioassays. Additionally, the limited availability of detection resources leads to increased costs and reduced efficiency in conducting bioassays. To ensure accurate and efficient execution of bioassays, this paper defines fluid scheduling tampering and activation sequences tampering as two types of security threats and proposes an Anomaly Detection method based on Discrete Particle Swarm Optimization (AD-DPSO) for CFMBs. The AD-DPSO method presents a weight calculation strategy based on fluid scheduling and a checkpoint selection strategy based on DPSO to effectively deploy checkpoints on the biochips. The weight calculation strategy ensures effective and secure checkpoint deployment strategies by favoring units with high usage frequency and low detection cost. The checkpoint selection strategy comprehensively considers chip resources and security requirements, thus maximizing the probability of anomaly detection while minimizing associated costs. Compared to the existing work, the proposed AD-DPSO achieves higher Return on Investment and lower detection costs with high detection probability.