Hybrid Data Mining Approaches for Intrusion Detection in the Internet of Things (CROSBI ID 667354)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Oreški, Dijana ; Andročec, Darko
engleski
Hybrid Data Mining Approaches for Intrusion Detection in the Internet of Things
Internet of things devices and services are often not designed with security in mind. For this reason, malicious users can create botnets and other malicious software targeting things’ vulnerabilities. In this work, we have tested various data mining techniques and proposed one that gives representing intrusion detection results with small percentage of false positives. Development of a successful prediction model largely depends on data preprocessing phase. Feature reduction implemented as feature extraction or feature selection is main step of preprocessing phase. This paper compares the applications of principal component analysis as feature extraction method and Relief, Information Gain, Gini Index and SfFS as feature selection methods to reduce features for decision tree classification. By examining NSL-KDD data set, the experiment shows that decision trees by feature selection using SfFS can perform significantly better than other approaches.
intrusion detection ; data mining ; Internet of things ; feature selection ; security
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
221-226.
2018.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of International Conference on Smart Systems and Technologies 2018 (SST 2018)
Žagar, Drago ; Martinović, Goran ; Rimac Drlje, Snježana ; Galić, Irena
Osijek: Faculty of Electrical Engineering, Computer Science and Information Technology
978-1-5386-7189-4
Podaci o skupu
International Conference on Smart Systems and Technologies 2018(SST 2018)
predavanje
10.10.2018-12.10.2018
Osijek, Hrvatska