Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms. (16th June 2019)

• Record Type: Journal Article
• Authors: Ren, Jiadong; Guo, Jiawei; Qian, Wang; Yuan, Huang; Hao, Xiaobing; Jingjing, Hu
• Other Names: Alazab Mamoun Academic Editor.
• Is Part Of: Security and communication networks. Volume 2019(2019)
• Publication Date: 2019-06-16
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2019/7130868
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Intrusion detection system (IDS) can effectively identify anomaly behaviors in the network; however, it still has low detection rate and high false alarm rate especially for anomalies with fewer records. In this paper, we propose an effective IDS by using hybrid data optimization which consists of two parts: data sampling and feature selection, called DO_IDS. In data sampling, the Isolation Forest (iForest) is used to eliminate outliers, genetic algorithm (GA) to optimize the sampling ratio, and the Random Forest (RF) classifier as the evaluation criteria to obtain the optimal training dataset. In feature selection, GA and RF are used again to obtain the optimal feature subset. Finally, an intrusion detection system based on RF is built using the optimal training dataset obtained by data sampling and the features selected by feature selection. The experiment will be carried out on the UNSW-NB15 dataset. Compared with other algorithms, the model has obvious advantages in detecting rare anomaly behaviors.