Real-Time Anomaly Detection in Large-Scale Sensor Networks using Isolation Forests

This research delves into real-time anomaly detection in enormous-scope sensor networks, employing Isolation Forest, One-Class SVM, Local Outlier Factor, and Recursive Partitioning algorithms. The review features Isolation Forest as a champion entertainer, accomplishing an accuracy of 0.92 and a review of 0.88 . One-Class SVM and Local Outlier Factor display cutthroat abilities with accuracy upsides of 0.87 and 0.89 , and review upsides of 0.82 and 0.85 , individually. Recursive Partitioning, stressing interpretability, keeps a good accuracy of 0.82 and a review of 0.76 . The examination stretches out to related work, investigating imaginative methodologies, for example, Subspace Occasional Grouping, profound exchange learning for modern shortcoming findings, and anomaly detection in unambiguous spaces like lodgings and microservicebased frameworks. Multimodal information combination, AI for 5G-based IoT network security, and novel structures like TCFTrans add to the rich scene of anomaly detection.