Future Internet

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods.

Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To address these challenges, this study presents an unsupervised, cloud-centric intrusion diagnosis framework that integrates autoencoder-based representation learning with density-based attack categorization. A dual-stage autoencoder is trained exclusively on benign traffic to learn compact latent representations and to identify anomalous flows using reconstruction-error analysis, enabling effective anomaly detection without prior attack labels. The detected anomalies are subsequently grouped using density-based learning to uncover latent attack structures and support fine-grained multiclass intrusion diagnosis under varying attack densities. Experiments conducted on the large-scale CSE-CIC-IDS2018 dataset demonstrate that the proposed framework achieves an anomaly detection accuracy of 99.46%, with high recall and low false-negative rates in the optimal latent-space configuration. The density-based classification stage achieves an overall multiclass attack classification accuracy of 98.79%, effectively handling both majority and minority attack categories. Clustering quality evaluation reports a Silhouette Score of 0.9857 and a Davies–Bouldin Index of 0.0091, indicating strong cluster compactness and separability. Comparative analysis against representative supervised and unsupervised baselines confirms the framework’s scalability and robustness under highly imbalanced cloud traffic, highlighting its suitability for future Internet cloud security ecosystems.

This paper presents a controlled comparative evaluation of SQL and NoSQL persistence mechanisms in containerized microservice architectures under variable workload conditions. Three persistence configurations—SQL with indexing, SQL without indexing, and a document-oriented NoSQL database, including supplementary hybrid SQL variants used for robustness analysis—are assessed across read-dominant, write-dominant, and mixed workloads, with concurrency levels ranging from low to high contention. The experimental setup is fully containerized and executed in a single-node environment to isolate persistence-layer behavior and ensure reproducibility. System performance is evaluated using multiple metrics, including percentile-based latency (p95), throughput, CPU utilization, and memory consumption. The results reveal distinct performance trade-offs among the evaluated configurations, highlighting the sensitivity of persistence mechanisms to workload composition and concurrency intensity. In particular, indexing strategies significantly affect read-heavy scenarios, while document-oriented persistence demonstrates advantages under write-intensive workloads. The findings emphasize the importance of workload-aware persistence selection in microservice-based systems and support the adoption of polyglot persistence strategies. Rather than providing absolute performance benchmarks, the study focuses on comparative behavioral trends that can inform architectural decision-making in practical microservice deployments.