## 2 Related Work
### Research on Dynamic Heterogeneous Redundancy Architecture  
Research on dynamic heterogeneous redundancy architecture has evolved significantly from traditional static heterogeneous redundancy systems, which fundamentally depend on the deployment of hardware and software components featuring distinct operational principles while maintaining functional equivalence within varied developmental frameworks to establish fault-tolerant infrastructures [45]. Nevertheless, such traditional architectures exhibit fundamental static properties, making them susceptible to complete system failure when malicious actors gain control over a majority of the constituent devices. Therefore, the primary innovation underlying DHR architecture lies in the incorporation of diverse heterogeneities spanning multiple spatial and temporal dimensions, effectively breaking the patterns of static behavior, homogeneity, and predictability that characterize conventional systems, thus enabling the simultaneous achievement of enhanced system dependability and robust defensive capabilities against adversarial threats [46]. Contemporary research on DHR architecture has focused on advancing three interconnected dimensions: heterogeneity characterization, intelligent scheduling mechanisms, and adaptive arbitration algorithms. Table 1 presents a comprehensive comparison of representative DHR research approaches, highlighting their methodological differences, privacy protection capabilities, and application domains. In the realm of heterogeneity measurement, [PERSON] et al. introduced location-aware metrics, including common-mode index and transfer probability, to quantify heterogeneity levels and guide executor selection strategies [47]. Addressing the practical challenges of variant selection in N-versioning systems, [PERSON] et al. developed HSelect, a novel heterogeneity measurement approach that detects code changes at file and block levels to generate heterogeneity matrices, enabling the selection of maximally diverse variants and effectively mitigating common-mode failure vulnerabilities compared to traditional selection methods [48]. [PERSON] et al. developed a comprehensive executor selection algorithm that integrates historical credibility and dissimilarity clustering (HCDC), employing clustering techniques to maximize differences between heterogeneous executor pools while dynamically updating credibility scores through negative feedback control mechanisms [49]. [PERSON] et al. proposed a mimic scoring voting algorithm that incorporates both heterogeneity measures and historical confidence as decision factors, demonstrating significant improvements in security performance while maintaining effectiveness even under low-load conditions with minimal scheduled executors [50]. To address the real-time constraints of industrial applications, [PERSON] et al. designed a multi-level 2-mode judgment algorithm that dynamically balances decision efficiency and result reliability through a \"process-first-arbitrate-later\" approach, progressively adjusting component confidence levels based on arbitration outcomes [44]. [PERSON] et al. introduced a polymorphic multi-mode decision algorithm based on AdaBoost classifiers, which overcomes traditional majority voting limitations by adaptively selecting optimal execution entities through comprehensive consideration of execution body information, historical performance, and output results [51]. Most recently, [PERSON] et al. proposed an active defense arbitration method based on adaptive anomaly sensing, which directly assesses device output reliability through deep learning-based anomaly detection rather than traditional device reliability evaluation, thereby enhancing arbitration accuracy while reducing common-mode escape vulnerabilities [46]. Despite these advances, current DHR research faces two fundamental limitations that impede its effectiveness in cloud-edge collaboration ICS environments.