Hybrid Feature Selection Techniques in Intrusion Detection System

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Singh:2023:UPCON,

• author = "Harvinder Singh and Sunita Beniwal and Dharminder Kumar",
• booktitle = "2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)",
• title = "Hybrid Feature Selection Techniques in Intrusion Detection System",
• year = "2023",
• volume = "10",
• pages = "988--990",
• abstract = "Network and information security have become difficult problems as a result of the network's phenomenal expansion. The main aim of intrusion detection is to detect and prevent security flaws in information systems. Intrusion detection system works with a vast volume of data. The processing time required by intrusion detection systems is high and detection rate is poor if all the features are used. Therefore, these unnecessary and redundant elements must be removed in order to improve intrusion detection system effectiveness. Three hybrid feature selection strategies are used in this study to choose features. K-Nearest Neighbour, Support Vector Machines, and Genetic Programming are used to analyse and evaluate the performance of selected features.",
• keywords = "genetic algorithms, genetic programming, Intrusion detection, Support vector machine classification, Feature extraction, Computer networks, Performance analysis, Information systems, detection, Support Vector Machines, SVM, K-Nearest Neighbour, KNN",
• DOI = "doi:10.1109/UPCON59197.2023.10434892",
• ISSN = "2687-7767",
• month = dec,
• notes = "Also known as \cite{10434892}",

}

Genetic Programming entries for Harvinder Singh Sunita Beniwal Dharminder Kumar

Citations