The field of anomaly detection and predictive modeling is witnessing significant advancements, driven by the development of innovative machine learning frameworks and techniques. Researchers are focusing on creating robust and adaptive models that can effectively handle complex, dynamic, and high-dimensional data. A key direction in this field is the integration of unsupervised and semi-supervised learning approaches, which enable models to learn from limited labeled data and adapt to evolving patterns. Another important trend is the application of generative models, such as GANs, to improve the detection of anomalies and enhance predictive capabilities. Furthermore, the use of continual learning and self-supervised learning techniques is becoming increasingly popular, as it allows models to incrementally learn from new data and retain previously acquired knowledge. Noteworthy papers in this area include:

• Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks, which proposes a novel framework for detecting anomalies in water distribution networks using a combination of LSTM-VAE and dual drift detection mechanisms.
• Towards Continual Visual Anomaly Detection in the Medical Domain, which explores the application of continual learning for visual anomaly detection in medical imaging and achieves state-of-the-art performance.
• MixGAN, which introduces a hybrid semi-supervised and generative approach for DDoS detection in cloud-integrated IoT networks and demonstrates superior robustness and generalizability.
• CITADEL, which proposes a self-supervised continual learning framework for anomaly detection in IoT intrusion detection and achieves significant improvements over existing methods.
• RANGAN, which integrates a GAN with a transformer architecture for anomaly detection in 5G Cloud RAN and achieves promising detection accuracy.