Collaborative Anomaly Detection

Collaborative anomaly detection focuses on leveraging data from multiple sources to improve the accuracy and robustness of anomaly detection systems, addressing limitations of single-source approaches. Current research explores diverse model architectures, including autoencoders, clustering algorithms, graph convolutional networks, and ensemble methods like locality-sensitive hashing, often applied within hybrid cloud-edge frameworks. This approach is particularly valuable in applications like smart homes, recommendation systems, and industrial sensor networks, enhancing security and reliability by improving the detection of malicious activities or unusual user behavior while mitigating privacy concerns. The development of efficient and privacy-preserving collaborative methods represents a significant advancement in anomaly detection capabilities.