Cloud platforms generate massive API access logs, where abnormal patterns may indicate security breaches, insider threats, or compromised credentials, demanding intelligent and automated anomaly detection mechanisms. Conventional approaches employ segmentation, statistical profiling, clustering, recurrent networks, and supervised classifiers to model sequential API behavior and distinguish normal activities from malicious deviations. These techniques generally achieve high detection accuracy and improved threat visibility, enhancing cybersecurity monitoring systems while reducing manual auditing efforts in large-scale distributed cloud environments. However, they struggle with evolving attack patterns, high false-positive rates, limited temporal dependency modelling, data imbalance, and poor generalization across heterogeneous cloud infrastructures. This study proposes a self-supervised Temporal Convolutional Network with adaptive anomaly scoring, achieving robust sequential modelling, reduced false alarms, and improved detection stability in cloud APIs. A self-supervised Temporal Convolutional Network models sequential API behavior using causal dilated convolutions and adaptive scoring, enabling accurate, scalable, and real-time cloud anomaly detection.

Industrial IoT systems generate massive telemetry streams, requiring intelligent predictive maintenance models to detect failures early, reduce downtime, and improve operational reliability and safety. Traditional approaches employ statistical analysis, sequence segmentation techniques, CNN-LSTM hybrids, and graph-based classification models to capture spatial-temporal dependencies and identify abnormal device behaviour patterns. These methods typically achieve high classification accuracy but often exhibit moderate RUL estimation performance, demonstrating strong fault detection capability across industrial, energy, and smart infrastructure applications. However, static graph structures, limited temporal attention, imbalanced fault distributions, and poor generalization under noisy conditions restrict robustness and real-world deployment scalability. This paper proposes a dynamic graph-based GAT-BiLSTM with cross-attention and gated fusion, achieving 99.50% accuracy and superior RUL prediction stability under noisy conditions. The framework incorporates adaptive adjacency learning and multi-task optimization to enhance predictive maintenance accuracy and robustness in IoT sensor networks.