Application of Machine Learning Methods for Timely Detection and Analysis of Anomalies

EasyChair Preprint 15239

Abstract

This work investigates the application of machine learning for anomaly detection in control systems, focusing on autoencoders as a tool for identifying and analyzing anomalies. The relevance of the research is driven by the need to enhance cybersecurity and the reliability of systems managing critically important production processes. The developed approach allows not only to determine the presence of an anomaly but also to identify key parameters contributing to its occurrence. The results demonstrate the effectiveness of using machine learning to improve the safety and reliability of automated systems. The contribution of this work lies in the development of a methodology for interpreting autoencoder data, providing a deep analysis of the causes of anomalies in technological processes.

Keyphrases: Anomaly analysis, Autoencoders, anomaly detection algorithms, machine learning, security threat assessment, statistical methods

@booklet{EasyChair:15239,
  author    = {Anzhelika Stakhova and Yurii Kyrychuk and Nataliia Nazarenko},
  title     = {Application of Machine Learning Methods for Timely Detection and Analysis of Anomalies},
  howpublished = {EasyChair Preprint 15239},
  year      = {EasyChair, 2024}}