Adaptive noise injection with joint self-supervised learning based anomaly detection for fluctuating IoT environment

A core challenge in anomaly detection for Internet of Things (IoT) scenarios lies in the extreme scarcity of labeled samples in real-world deployments. Most existing methods rely on unsupervised paradigms to learn normal behavior patterns of IoT devices. However, such approaches often define overly strict boundaries for “normality,” making minor fluctuations prone to be misjudged as anomalies and resulting in high false positive rates, which severely undermines the reliability and operational efficiency of cloud-side monitoring systems. To this end, an improved self-supervised task learning framework is proposed specifically for IoT anomaly detection. In our work, an adaptive noise injection mechanism is designed for data augmentation, which automatically adjusts noise intensity according to the dynamic characteristics of input signals to simulate diverse anomaly patterns that closely resemble real-world scenarios. We then propose a joint learning enhanced paradigm that integrates contrastive learning with a parallel reconstruction task. Specifically, the InfoNCE loss is introduced to enhance the efficiency of contrastive learning in extracting discriminative features, while a novel loss function is devised to jointly optimize the two tasks. The resulting representation learning model maintains high sensitivity to anomaly deviations while preserving strong generalization capability to normal behavioral patterns, making it well-suited for the complex and dynamic IoT environment under cloud-edge collaboration. Finally, a classifier based on neighbor/non-neighbor representations is built to perform self-supervised anomaly discrimination. Comparative experimental results on five public IoT monitoring datasets, including MSL, SMAP, SMD, SWAT, and WADI, demonstrate that the proposed method achieves state-of-the-art F1-score and AU-PR on four datasets, with average improvements of 4.65% in F1-score and 2.04% in AU-PR over the strongest baseline, validating its effectiveness for IoT anomaly detection.