A Novel Hybrid Data Reduction Strategy and Its Application to Intrusion Detection
| Autor: | Raudel Hernández-León, Osvaldo Andrés Pérez-García, Andrés Gago-Alonso, Vitali Herrera-Semenets |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Reduction strategy
021103 operations research Computer science media_common.quotation_subject 0211 other engineering and technologies Telecommunications service Feature selection 02 engineering and technology Intrusion detection system computer.software_genre Reduction (complexity) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Data pre-processing Data mining computer Data reduction media_common |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319751924 CIARP |
| DOI: | 10.1007/978-3-319-75193-1_35 |
| Popis: | The presence of useless information and the huge amount of data generated by telecommunication services can affect the efficiency of traditional Intrusion Detection Systems (IDSs). This fact encourage the development of data preprocessing strategies for improving the efficiency of IDSs. On the other hand, improving such efficiency relying on the data reduction strategies, without affecting the quality of the reduced dataset (i.e. keeping the accuracy during the classification process), represents a challenge. Also, the runtime of commonly used strategies is usually high. In this paper, a novel hybrid data reduction strategy is presented. The proposed strategy reduces the number of features and instances in the training collection without greatly affecting the quality of the reduced dataset. In addition, it improves the efficiency of the classification process. Finally, our proposal is favorably compared with other hybrid data reduction strategies. |
| Databáze: | OpenAIRE |
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