Data Preprocessing for Intrusion Detection System Using Encoding and Normalization Approaches
| Autor: | M Srikanth Yadav., R. Kalpana |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Normalization (statistics)
GeneralLiterature_INTRODUCTORYANDSURVEY business.industry Computer science Network security Deep learning InformationSystems_DATABASEMANAGEMENT 020206 networking & telecommunications 02 engineering and technology Intrusion detection system computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Categorization Virtual machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data pre-processing Data mining business computer |
| Zdroj: | 2019 11th International Conference on Advanced Computing (ICoAC). |
| DOI: | 10.1109/icoac48765.2019.246851 |
| Popis: | This work presents a data preparation and data preprocessing framework to support deep learning and network security experts in producing qualitative data for empirical experimental analysis of intrusion detection data. The Onehotencoder and min-max normalization approaches are used in this proposed preprocessing module. This research paper focuses primarily on analyzing two datasets, specifically KDD Cup' 99, and NSL-KDD datasets, which are commonly used to investigate computer network intrusion detection. The database of the KDD Cup ' 99 consist of five million files, each with 41 attributes that can categorize malicious intrusions into four classes: Probe, DoS, U2R and R2L. The real traffic data cannot be replicated by the KDD cup’99 data set because it was produced over a virtual computer network by simulation. Duplicate and obsolete records were omitted from training and testing in the NSL-KDD database from the KDD Cup ' 99 dataset. |
| Databáze: | OpenAIRE |
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