Deep learning for anomaly detection

• المؤلف الرئيسي: Tan, Kenneth Jun Wei
• مؤلفون آخرون: Wang Dan Wei
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2022
• الموضوعات: Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/157429
• المؤسسة: Nanyang Technological University

Anomaly detection methods are devoted to target detection schemes in which no priori information about the spectra of the targets of interest is available. This paper research on the 4 various types of anomaly detection machine learning anomaly models, namely Local Outlier Factor (LOF), Isolation Forest, One-Class Support Vector Machine (SVM), and Robust Covariance. Additionally, this paper shows the various steps in the implementation anomaly models and studies the effectiveness of each model in analysing an industrialized Multivariate Time-Series dataset.