Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset
| Autor: | B. M. Sagar, H. P. Vinutha, B. Poornima |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
InformationSystems_DATABASEMANAGEMENT 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Filter (signal processing) computer.software_genre Field (computer science) Intrusion ComputingMethodologies_PATTERNRECOGNITION Quartile Interquartile range Outlier 0202 electrical engineering electronic engineering information engineering Range (statistics) 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9789811075629 |
| DOI: | 10.1007/978-981-10-7563-6_53 |
| Popis: | Unpredictable usage of Internet adds more problems to the network. Protecting the system from the anomalous behavior plays a major issue in NIDS. Data mining approaches in the field of Intrusion Detection System (IDS) is becoming more popular. The outlier is a current problem faced by many data mining researches. Outliers are the patterns which are not in the range of normal behavior. Outliers in the dataset produce more false positive alarms, and this has to be reduced to increase the efficiency of IDS. We have used Interquartile Range technique to identify the outliers in the NSLKDD’99. In this, the continuous range of input is divided into quartiles and these quartiles are analyzed to target the range of outliers. Then the obtained outliers are removed by a filter called remove with value. The experiment is conducted using Weka data mining tool. |
| Databáze: | OpenAIRE |
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