METHODS AND ALGORITHMS FOR ANOMALY DETECTION

The article examines several approaches to finding anomalies in complicated datasets, an essential task in domains including industrial monitoring, cybersecurity, banking, and healthcare. The paper evaluates the theoretical underpinnings, benefits, and drawbacks of anomaly detection strategies, classifying them into statistical approaches, clustering techniques, deep learning models, and hybrid/ensemble methods.Although they provide theoretically solid methods for identifying outliers, statistical techniques such as Z-score analysis, Gaussian Mixture Models (GMM), and Kernel Density Estimation (KDE) have trouble handling high-dimensional and non-linear data. Whilst K-means, DBSCAN, and hierarchical clustering are popular clustering approaches for unsupervised anomaly detection, their efficacy depends on noise sensitivity and parameter choice.Additionally, the study looks at deep learning and machine learning methods, including autoencoders, Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks.Particularly for time-series and picture data, these models have revolutionized anomaly detection, yet their interpretability and computational efficiency present difficulties.Furthermore, hybrid and ensemble approaches combine many techniques to increase accuracy and resilience. In challenging anomaly detection tasks, methods such as stacking ensembles, boosting, and isolation forests show improved performance.Interpretability and computational requirements are still major issues.Many current research issues, such as explainability, model scalability, and adaptability to changing contexts, are highlighted in the article. Future research should concentrate on federated learning, explainable AI (XAI), and semi-supervised learning to ensure more transparent and effective anomaly detection models. This study lays the groundwork for future developments in the area.