Szczegóły publikacji
Opis bibliograficzny
Machine learning methods for anomaly detection in computer networks / Jakub GAJDA, Joanna KWIECIEŃ, Wojciech CHMIEL // W: MMAR 2022 [Dokument elektroniczny] : 26th international conference on Methods and Models in Automation and Robotics : 22–25 August 2022, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — Print on Demand (PoD) ISBN: 978-1-6654-6859-6. — e-ISBN: 978-1-6654-6858-9. — S. 276–281. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 280–281, Abstr. — Publikacja dostępna online od: 2022-09-08
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164355 |
|---|---|
| Data dodania do BaDAP | 2026-01-08 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR55195.2022.9874341 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
With a large increase in the amount of data that are transferred via publicly available computer networks, the global demand for new protection and prevention methods could be observed in recent studies of many research groups. The paper deals with anomaly detection, focusing on cybersecurity applications, as there are only few papers that address this topic. Four methods, such as DBSCAN, One-class SVM, LSTM and Isolation forest were used to solve this problem. During the experimental part, the implementation and experiments were performed to examine the performance on common dataset to assess the ability and further possible applications.
