Efficient algorithm for intrusion attack classification by analyzing KDD Cup 99

Author

Chandolikar, Mrs N S ; Nandavadekar, V.D.

Author_Institution

Dept. of Comput. Eng., Vishwakarma Inst. of Technol., Pune, India

fYear

2012

fDate

20-22 Sept. 2012

Firstpage

1

Lastpage

5

Abstract

Importance of intrusion detection system (IDS) for network security management is widely accepted. Efficiency of IDS is mainly affected by algorithms used for feature identification and classification. Data mining can be very fruitful for feature selection and intrusion detection. In this paper, we have presented J48 classification algorithm for intrusion detection. To evaluate the performance of the algorithm correctly classified instances, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Root relative squared error and kappa statistics measures are applied.

Keywords

computer network security; data mining; feature extraction; mean square error methods; pattern classification; statistical analysis; IDS; J48 classification algorithm; KDD Cup 99; MAE; RMSE; data mining; feature classification; feature identification; feature selection; intrusion attack classification; intrusion detection system; kappa statistic measures; mean absolute error; network security management; root mean squared error; root relative squared error; Algorithm design and analysis; Classification algorithms; Data mining; Decision trees; Feature extraction; Intrusion detection; Machine learning algorithms; Intrusion detection system (IDS); J48 algorithm; classification; data mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on

Conference_Location

Indore

ISSN

2151-7681

Print_ISBN

978-1-4673-1988-1

Type

conf

DOI

10.1109/WOCN.2012.6335546

Filename

6335546