Distributed Race Condition Detection in CPSS via Proactive DAG-Guided Hidden Markov Models

Modern cyber-physical-social systems (CPSS) increasingly rely on distributed cloud computing infrastructures to process massive, heterogeneous data streams. However, this architectural convergence introduces critical security challenges, with transient race conditions emerging as a persistent and elusive threat in these highly concurrent, distributed backends. To address this critical bottleneck, we propose a novel distributed intelligence framework for dynamic race condition detection. Our approach consists of: 1) a preprocessing module that utilizes directed acyclic graph (DAG)-based modeling to establish strict causal dependencies and filter concurrent event pairs; and 2) an active detection module that implements granular proactive scheduling to deterministically trigger race-prone interleavings. By integrating hidden Markov models (HMMs) for behavioral anomaly detection, our framework effectively identifies subtle concurrency issues in CPSS-based cloud environments. We validated our method on a curated dataset of 32 verified real-world vulnerabilities across six widely used distributed systems—Apache Hadoop2/Yarn, HDFS, HBase, Cassandra, Zookeeper, and Flink. Experimental results demonstrate that our approach achieves a 75.0% detection rate, significantly outperforming traditional fuzzing and static baselines ($\boldsymbol{p} \mathbf{\lt 0.01}$). Crucially, the framework maintains a statistically manageable low-latency runtime overhead (12.3%), offering an acceptable trade-off between detection rate and system efficiency. By transforming transient concurrent flaws into consistently reproducible vulnerabilities, this approach substantially enhances the security, reliability, and trust foundations necessary for the robust deployment of modern CPSS architectures.