Methods of Machine Learning in System Abnormal Behavior Detection
| Autor: | Alexey Ivutin, Pavel A. Savenkov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science media_common.quotation_subject Big data Intelligent decision support system Unstructured data Machine learning computer.software_genre Software Feature (computer vision) Information system Quality (business) Artificial intelligence business computer media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030539559 ICSI |
| DOI: | 10.1007/978-3-030-53956-6_45 |
| Popis: | The aim of the research is to develop mathematical and program support for detecting abnormal behavior of users. It will be based on analysis of their behavioral biometric characteristics. One of the major problems in UEBA/DSS intelligent systems is obtaining useful information from a large amount of unstructured, inconsistent data. Management decision-making should be based on real data collected from the analysed feature. However, based on the information received, it is rather difficult to make any management decision, as the data are heterogeneous and their volumes are extremely large. Application of machine learning methods in implementation of mobile UEBA/DSS system is proposed. This will make it possible to achieve a data analysis high quality and find complex dependencies in it. A list of the most significant factors submitted to the input of the analysing methods was formed during the research. |
| Databáze: | OpenAIRE |
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