The recent advancements in anomaly detection across various domains, including network security, natural language processing, and Internet of Things (IoT), demonstrate a significant shift towards leveraging deep learning and transformer-based models. These innovations are addressing critical challenges such as data imbalance, model interpretability, and the detection of unseen or evolving threats. In network security, the focus is on developing more fine-grained and scalable models that can handle complex scenarios, such as detecting network intrusions and webshell attacks. NLP-based anomaly detection is gaining traction with the introduction of comprehensive benchmarks that highlight the need for automated model selection and the superiority of transformer-based embeddings. In the IoT sector, deep learning models incorporating LSTM and attention mechanisms are proving effective in countering cyber threats by analyzing complex network data. Notably, the integration of bio-inspired optimization techniques and the application of large language models to network flow data are advancing the state-of-the-art in anomaly detection, offering robust solutions for real-world deployment. These developments collectively underscore a trend towards more intelligent, adaptive, and efficient anomaly detection systems that can operate in dynamic and resource-constrained environments.