We know how many rules are needed in this problem --- only one rule is sufficient. In this sence too, this test-set is a good one. The scheme of training only with normal sample with the exactly same set of 5 membership functions as the one described above, and an almost same way of genetic algorithm, Gomez et al applied this technique to the dataset from KDD CUP 99. That is, the dataset is 10\% version of KDD-CUP99 removing all the categorical attributes, which results in the 492021 data each made up of 33 attributes which are normalized between 0 and 1 using maximum and minimum values found --- also the same way as ours. An 80\% of the normal samples were picked randomly and used as training data set, while the remaining 20\% was used along with the abnormal samples as a testing set. Then they claimed, for example, the detection rate of 98.22\% while false alarm rate being 1.9\%. Wonderful result, isn't it? Comparing to our poor results, what is the difference? We have had so many such articles. Ayara, M., J. Timmis, R. D. Lemos, L. N. D. Castro, and R. Duncan (2002) "Negative Selection: How to Generate Detectors." Proceedings of 1st International Conference on Artificial Immune Systems (ICARIS) pp. 89--98. P. Helman and S. Forrest (1994) "An Efficient Algorithm for Generating Random Antibody Strings." Technical Report 94-07, University of New Mexico, Albuquerque, NM. P. D'haeseleer, S. Forrest, and P. Helman (1996) "An immunological approach to change detection: algorithms, analysis and implications." Proceedings of the 1996 IEEE Symposium on Computer Security and Privacy, pp. 110--119. F. Esponda, S. Forrest (2002) "Detector coverage under the r-contiguous bits matching rule." University of New Mexico Technical Report TR-CS-2002-03. F. Esponda, S. Forrest, and P. Helman (2004) "A formal framework for positive and negative detection." IEEE Transactions on Systems, Man, and Cybernetics 34:1 pp. 357-373. Clonal Selection J. Kim and P. Bentley "An Artificial Immune Model for Network Intrusion Detection" Positive Selection Kwee-Bo Sim and Dong-Wook Lee (2003) "Modeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm." International Journal of Control, Automation, and Systems Vol. 1, No. 4, pp. 453--458 Negative Selection Dasgupta, et al (1999) "An Anomaly Detection Algorithm Inspired by the Immune System." Dasgupta et al. (Eds), Artificial Immune System and Their Application Z. Ji and D. Dasgupata (2004) "Augmented Negative Selection Algorithm with Variable-Coverage Detectors." Proceedings of the Congress on Evolutionary Computation F. Gonzalez, D. Dasgupta (2003) "Anomaly Detection Using Real-Valued Negative Selection." Genetic Programming and Evolvable Machines, Vol. 4 No. 4, Kluwer Academic Press, pp 383-403. F. Gonzalez, D. Dasgupta and L. F. Nino (2003) "A Randomized Real-Valued Negative Selection Algorithm." Proceedings of the 2nd International Conference on Artificial Immune Systems, [12] Gonzalez, F., D. Dasgupta, J. Gomez (2003) "The Effect of Binary Matching Rules in Negative Selection." GECCO-03.
D. Dasgupta and N. S. Majumdar (2002) "Anomaly detection in Multidimensional Data using Negative Selection algorithm." Proceedings of the Congress on Evolutionary Computation Ceong, H. T., et al, (2003) "Complementary Dual Detectors for Effective Classification." ICARIS-03, pp.242-248. J. Kim and P. Bentley (2001) "An evaluation of negative selection in an artificial immune system for network intrusion detection." Proceedings Genetic Evolutionary Computation Conference Immuno-fuzzy Approach J. Gomez, F. Gonzalez, and D. Dasgupta (2003) "An Immuno-Fuzzy Approach to Anomaly Detection" proceedings of the 12th IEEE International Conference on Fuzzy Systems, Vol. 2, pp. 1219-1224. Fuzzy Rules not based on Immune System but Evolved by Genetic Algorithm J. Gomez and D. Dasgupta "Evolving Fuzzy Classifiers for Intrusion Detection" J. Gomez, O. Nasraoui, D. Dasgupta, and F. Gonzalez (2002) "Complete Expression Trees for Evolving Fuzzy Classifier Systems with Genetic Algorithms and Application to Network Intrusion Detection." Proceedings of the IEEE, North American Fuzzy Information Processing Society Conference on Fuzzy Learning. J. Gomez and D. Dasgupta (2002) "Using Competitive Operators and a Local Selection Scheme in Genetic Search." Proceedings (Late-Breaking) of the Genetic and Evolutionary Computation Conference, pp. 193-200. Messy-GA Approach F. HOFFMANN Soft Computing Techniques for the Design of Mobile Robot Be- haviours" F. Hofmann and G. Pfister (1995) "A New Learning Method for the Design of Hierarchical Fuzzy Controllers Using Messy Genetic Algorithms" M. M. Chowdhury and Yun Li "Messy Genetic Algorithm Based New Learning Method for Structurally Optimised Neurofuzzy Controllers" Yet Another Intrusion Detection --- Data Mining Approach W. Lee and S. Stolfo (1998) "Data mining approaches for intrusion detection" Proceedings of the 7th USENIX security symposium (San Antonio, TX).