Network Anomaly Detection Using Genetic Programming with Semantic Approximation Techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Chu:2021:RIVF,

• author = "Thi Huong Chu and Quang {Uy Nguyen}",
• title = "Network Anomaly Detection Using Genetic Programming with Semantic Approximation Techniques",
• booktitle = "2021 RIVF International Conference on Computing and Communication Technologies (RIVF)",
• year = "2021",
• abstract = "Network anomaly detection aims at detecting malicious behaviors to the network systems. This problem is of great importance in developing intrusion detection systems to protect networks from intrusive activities. Recently, machine learning-based methods for anomaly detection have become more popular in the research community thanks to their capability in discovering unknown attacks. In the paper, we propose an application of Genetic Programming (GP) with the semantics approximation technique to network anomaly detection. Specifically, two recently proposed techniques for reducing GP code bloat, i.e. Subtree Approximation (SA) and Desired Approximation (DA) are applied for detecting network anomalies. SA and DA are evaluated on 6 datasets in the field of anomaly detection and compared with standard GP and five common machine learning methods. Experimental results show that SA and DA have achieved better results than that of standard GP and the performance of GP is competitive with other machine learning algorithms.",
• keywords = "genetic algorithms, genetic programming, Learning systems, Machine learning algorithms, Semantics, Intrusion detection, Machine learning, Communications technology, Semantic Approximation, Network Anomaly Detection",
• DOI = "doi:10.1109/RIVF51545.2021.9642140",
• ISSN = "2162-786X",
• month = aug,
• notes = "Also known as \cite{9642140}",

}

Genetic Programming entries for Thi Huong Chu Quang Uy Nguyen

Citations