Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms

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 t...

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Bibliographic Details
Published in:Security and communication networks 2019, Vol.2019 (2019), p.1-11
Main Authors: Hao, Xiaobing, Yuan, Huang, Qian, Wang, Guo, Jiawei, Ren, Jiadong, Hu, Jing-jing
Format: Article
Language:English
Subjects:
Accuracy
Anomalies
Artificial intelligence
Behavior
Classification
Data sampling
Datasets
Decision trees
Digital signatures
False alarms
Feature selection
Fuzzy logic
Genetic algorithms
Graphs
Hybrid systems
Intrusion detection systems
Machine learning
Optimization
Outliers (statistics)
Parameter estimation
Support vector machines
Training
Wireless networks
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Summary: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.
ISSN:1939-0114
1939-0122
DOI:10.1155/2019/7130868