| Autor: |
Hussein, Maher Khalaf, ALkahla, Lubna Thanoon, Alqassab, Asmaa |
| Předmět: |
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| Zdroj: |
Iraqi Journal for Computers & Informatics Ijci; 2024, Vol. 50 Issue 1, p46-53, 8p |
| Abstrakt: |
This investigation aims to explore previous research on the implementation of feature selection in intrusion detection. Feature selection has demonstrated its ability to enhance or sustain comparable classification accuracy levels for intrusion detection systems, while simultaneously improving classification efficiency. The evaluation includes an assessment of filter-based, wrapper- based, and hybrid feature selection techniques. Given that Big Data challenges can affect intrusion detection, feature selection's classification efficiency can aid in lowering computing requirements. Older KDD intrusion detection datasets have received considerable attention in previous feature selection research. Consequently, researchers need more high-quality datasets that are available to the general public. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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