<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.</p></abstract>

Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of...

Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

Online veröffentlicht: 19. Sept. 2018

Seitenbereich: 111 - 119

Eingereicht: 22. Jan. 2018

Akzeptiert: 30. Juli 2018

DOI: https://doi.org/10.2478/cait-2018-0042

Schlüsselwörter
Traffic anomaly ids, genetic algorithm, feature extraction, feature selection, classification

© 2018 L. Gnanaprasanambikai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

Online veröffentlicht: 19. Sept. 2018

Seitenbereich: 111 - 119

Eingereicht: 22. Jan. 2018

Akzeptiert: 30. Juli 2018

DOI: https://doi.org/10.2478/cait-2018-0042

SchlüsselwörterTraffic anomaly ids, genetic algorithm, feature extraction, feature selection, classification

© 2018 L. Gnanaprasanambikai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Traffic anomaly ids, genetic algorithm, feature extraction, feature selection, classification