| Title | Automated Process Control Anomaly Detection Using Machine Learning Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Dmitrievich, Asyaev Grigorii, Nikolaevich, Sokolov Aleksandr |
| Conference Name | 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) |
| Keywords | Anomaly, Automated Response Actions, Big Data, composability, computer attack, machine learning, Mathematical model, pubcrawl, Resiliency, Vulnerability |
| Abstract | The paper discusses the features of the automated process control system, defines the algorithm for installing critical updates. The main problems in the administration of a critical system have been identified. The paper presents a model for recognizing anomalies in the network traffic of an industrial information system using machine learning methods. The article considers the network intrusion dataset (raw TCP / IP dump data was collected, where the network was subjected to multiple attacks). The main parameters that affect the recognition of abnormal behavior in the system are determined. The basic mathematical models of classification are analyzed, their basic parameters are reviewed and tuned. The mathematical model was trained on the considered (randomly mixed) sample using cross-validation and the response was predicted on the control (test) sample, where the model should determine the anomalous behavior of the system or normal as the output. The main criteria for choosing a mathematical model for the problem to be solved were the number of correctly recognized (accuracy) anomalies, precision and recall of the answers. Based on the study, the optimal algorithm for recognizing anomalies was selected, as well as signs by which this anomaly can be recognized. |
| DOI | 10.1109/USBEREIT48449.2020.9117692 |
| Citation Key | dmitrievich_automated_2020 |
Automated Process Control Anomaly Detection Using Machine Learning Methods