Intrusion Detection in Computer Networks via Machine Learning Algorithms - Ertam, Fatih; Kilincer, Ilhan Firat; Yaman, Orhan (Ieee, 2017)
With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether the data received on the internet is normal or abnormal data. In order to achieve this goal, the KDD Cup 99 data set which is frequently used in literature studies is classified by Naive Bayes (NB), bayes NET (bN), Random Forest (RF), Multilayer Perception (MLP) and Sequential Minimal Optimization (SMO) algorithms. Classifiers are also compared with false rate, precision, recall, and F measure metrics along with accuracy rate values. Classification times of classifiers are also given by comparison.
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Keywords: machine learning; classification; intrusion detection; classification metrics; network security