Cyberattacks Detection and Analysis in a Network Log System Using XGBoost with ELK Stack

Abstract

Abstract The usage of artificial intelligence and machine learning methods on cyberattacks increasing significantly recently. For the defense method of cyberattacks, it is possible to detect and identify the attack event by observing the log data and analyzing whether it has abnormal behavior or not. This paper implemented the ELK Stack network log system (NetFlow Log) to visually analyze log data and present several network attack behavior characteristics for further analysis. Additionally, this system evaluated the extreme gradient enhancement (XGBoost), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) model for machine learning methods. Keras was used as a deep learning framework for building a model to detect the attack event. From the experiments, it can be confirmed that the XGBoost model has an accuracy rate of 96.01% for potential threats. The full attack data set can achieve 96.26% accuracy, which is better than RNN and DNN models.