Problems of KDD Cup 99 Dataset Existed and Data Preprocessing
作者： Yan Wang , Kun Yang , Xiang Jing , Huang Long Jin
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.667.218
关键词: Process (engineering) 、 Intrusion detection system 、 Data mining 、 Attack model 、 Training set 、 Benchmark (computing) 、 De facto 、 Data pre-processing 、 Intrusion 、 Engineering
摘要: KDD Cup 99 dataset is not only the most widely used in intrusion detection, but also de facto benchmark on evaluating performance merits of detection system. Nevertheless there are a lot issues this which cannot be omitted. In order to establish good data mining models and find appropriate network attack types’ features, researchers should have well-known understanding dataset. paper, first foremost we made an in-depth analysis problems existed, given related solutions. Secondly, carried out plenty preprocessing 10% subset dataset’s training set, giving better results following process. What’s more, by comparing 10 common kinds algorithms our experiment, analyzed summarized that plays vital role importance algorithms.
sci-hub.se 参考文章(18)
Adetunmbi Adebayo Olusola, Oladele S Adeola, Oladuni Abosede Daramola, None, Relevance Features Selection for Intrusion Detection Springer, New York, NY. pp. 407- 418 ,(2011) , 10.1007/978-1-4614-0373-9_31
Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Sal Stolfo, A Geometric Framework for Unsupervised Anomaly Detection Applications of Data Mining in Computer Security. pp. 77- 101 ,(2002) , 10.1007/978-1-4615-0953-0_4
E Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy, Sal Stolfo, A GEOMETRIC FRAMEWORK FOR UNSUPERVISED ANOMALY DETECTION: DETECTING INTRUSIONS IN UNLABELED DATA APPLICATIONS OF DATA MINING IN COMPUTER SECURITY. pp. 0- 0 ,(2002) , 10.7916/D8D50TQT
George H John, Ron Kohavi, Karl Pfleger, None, Irrelevant Features and the Subset Selection Problem Machine Learning Proceedings 1994. pp. 121- 129 ,(1994) , 10.1016/B978-1-55860-335-6.50023-4
Maheshkumar Sabhnani, Gursel Serpen, Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set intelligent data analysis. ,vol. 8, pp. 403- 415 ,(2004) , 10.3233/IDA-2004-8406
John McHugh, Testing Intrusion detection systems ACM Transactions on Information and System Security. ,vol. 3, pp. 262- 294 ,(2000) , 10.1145/382912.382923
Klaus-Robert Müller, Christin Schäfer, Pavel Laskov, Igor V. Kotenko, Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines Praxis Der Informationsverarbeitung Und Kommunikation. ,vol. 27, pp. 228- 236 ,(2004) , 10.17877/DE290R-15912
Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, Ali A. Ghorbani, A detailed analysis of the KDD CUP 99 data set computational intelligence and security. pp. 53- 58 ,(2009) , 10.1109/CISDA.2009.5356528
Isabelle Guyon, André Elisseeff, An introduction to variable and feature selection Journal of Machine Learning Research. ,vol. 3, pp. 1157- 1182 ,(2003) , 10.1162/153244303322753616
A.H. Sung, S. Mukkamala, Identifying important features for intrusion detection using support vector machines and neural networks symposium on applications and the internet. pp. 209- 216 ,(2003) , 10.1109/SAINT.2003.1183050