Machine Learning-Based Hybrid Feature Selection for Improvised Network Intrusion Detection

Pritimayee Satapathy; Prafulla Kumar Behera

doi:10.1007/978-3-031-23233-6_25

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Machine Learning-Based Hybrid Feature Selection for Improvised Network Intrusion Detection

Pritimayee Satapathy Prafulla Kumar Behera

Year: 2022 Communications in computer and information science Pages: 333-347 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-23233-6_25

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Keywords:

Computer science Feature selection Intrusion detection system Akaike information criterion Machine learning Artificial intelligence C4.5 algorithm Information gain ratio Anomaly detection Network security Data mining Selection (genetic algorithm) Model selection Support vector machine Naive Bayes classifier

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Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Internet Traffic Analysis and Secure E-voting

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

Machine Learning-Based Hybrid Feature Selection for Improvised Network Intrusion Detection

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