http (KDDCUP99) dataset

Dataset Information

The original KDD Cup 1999 dataset from UCI machine learning repository contains 41 attributes (34 continuous, and 7 categorical), however, they are reduced to 4 attributes (service, duration, src_bytes, dst_bytes) as these attributes are regarded as the most basic attributes (see kddcup.names), where only ‘service’ is categorical. Using the ‘service’ attribute, the data is divided into {http, smtp, ftp, ftp_data, others} subsets. Here, only ‘http’ service data is used. Since the continuous attribute values are concentrated around ‘0’, we transformed each value into a value far from ‘0’, by y = log(x + 0.1). The original data set has 3,925,651 attacks (80.1%) out of 4,898,431 records. A smaller set is forged by having only 3,377 attacks (0.35%) of 976,157 records, where attribute ‘logged_in’ is positive. From this forged dataset 567,497 ‘http’ service data is used to construct the http (KDDCUP99) dataset. 

Source (citation)

Kenji Yamanishi, Jun-Ichi Takeuchi, Graham Williams, and Peter Milne. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 320–324, New York, NY, USA, 2000. ACM Press.

Graham Williams, Rohan Baxter, Hongxing He, Simon Hawkins, and Lifang Gu. A comparative study of rnn for outlier detection in data mining. In Proceedings of the 2002 IEEE International Conference on Data Mining, page 709, Washington, DC, USA, 2002. IEEE Computer Society.

Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. “Isolation forest.2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008.

K. M. Ting, J. T. S. Chuan, and F. T. Liu. “Mass: A New Ranking Measure for Anomaly Detection.“, IEEE Transactions on Knowledge and Data Engineering, 2009.

Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu and Swee Chuan Tan, Mass Estimation and Its Applications. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 989-998, Washington, DC, 2010.

Swee Chuan Tan, Kai Ming Ting & Fei Tony Liu. (2011). Fast Anomaly Detection for Streaming Data. Proceedings of the International Joint Conference on Artificial Intelligence 2011. pp.1151-1156.


File: http.mat

Description: X = Multi-dimensional point data, y = labels (1 = outliers, 0 = inliers)