Network intrusion detection using machine learning anomaly detection algorithms
Mine Elif Karsligil, Ali Gökhan YAVUZ, Mehmet Amac Guvensan, Khadija Hanifi, Hasan Bank · 2017
Attacks on the network are exceptional cases that are not observed in normal traffic behavior. In this work, in order to detect network attacks, using k-means algorithm a new semi-supervised anomaly detection system has been designed and implemented. During the training phase, normal samples were separated into clusters by applying k-means algorithm. Then, in order to be able to distinguish between normal and abnormal samples — according to their distances from the clusters' centers and using a validation dataset-a threshold value was calculated. New samples that are far from the clusters' centers more than the threshold value is detected as anomalies. We used NSL-KDD — a labelled dataset of network connection traces-for testing our method's effectiveness. The experiments result on the NSL-KDD data set, shows that we achieved an accuracy of 80.119%.