2026-05-11T08:13:27.277Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:2543 2014-10-16T11:07:51Z 1

Application of Hybrid Incremental Machine Learning Methods to Anomaly Based Intrusion Detection 2014 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/2543 http://etran.etf.rs/index_e.html V. Miškovic M. Milosavljević S. Adamović A. Jevremović Anomaly based intrusion detection systems can detect computer systems misuse based on network and system behaviour where the type of misuse isn’t previously known. Different machine learning methods explore different hypothesis spaces, use different search strategies, different sets of features, and are appropriate for different types of problems. Their combination or integration usually gives better performance than using each individual machine learning method on its own. Hybrid models can reduce individual limitations of basic models and can exploit their different generalization mechanisms. In this paper we compare performances of explicit, implicit, and hybrid machine learning models in several publicly available intrusion detection problems. Their applicability to mobile and cloud computing is briefly analyzed. Machine learning methods in use areprovided by the Weka/MOA and R/Revolution environments conferenceObject VII2.3.1 6 Proceedings of 1st International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2014