Please use this identifier to cite or link to this item:
https://research.matf.bg.ac.rs/handle/123456789/2146| Title: | Machine Learning based Network Anomaly Detection for IoT environments | Authors: | Timčenko, Valentina Gajin, Slavko |
Affiliations: | Informatics and Computer Science | Issue Date: | 2018 | Rank: | M33 | Publisher: | Belgrade : Information Society of Serbia | Related Publication(s): | Proceedings of the 8th International Conference on Information Society and Technology ICIST 2018 | Conference: | International Conference on Information Society and Technology ICIST 2018 (8 ; 2018 ; Kopaonik) | Abstract: | This paper focuses on the problem of providing security measures, anomaly detection, and prevention to the emerging IoT environment. We have considered several different categories of machine learning classification algorithms with a goal to estimate the proper choice for network anomaly detection in IoT like environments. The special focus is on SVM and the set of bagging and boosting algorithms, the analysis of their performance and further comparison taking for the reference the modern, IoT like, UNSW-NB15 dataset. |
URI: | https://research.matf.bg.ac.rs/handle/123456789/2146 |
| Appears in Collections: | Research outputs |
Show full item record
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.