Detection of Intrusions with Machine Learning Methods
Beyzanur Bostancı, Ahmet Albayrak · 2021
Today, especially with the emergence of social networks and IoT technologies, big data has entered the literature. With the development of technology, the size of the data has increased and accordingly data security gaps have emerged. In this study, Support Vector Machines and Random Forest algorithms, which are Supervised Machine Learning Algorithms, were used to analyze a data set consisting of unauthorized network logins. As a result of the experimental studies, it was observed that both algorithms produced good results, but the Random Forest approach produced better results.