HOW MANY PARACHUTISTS WILL BE NECESSARY TO FIND A NEEDLE IN A PASTORAL - WHO IS A LUCKY ONE?

Authors

  • Akira Imada

DOI:

https://doi.org/10.47839/ijc.5.3.417

Keywords:

Network intrusion detection, machine learning, training and testing, Iris dataset, KDD cup 99 dataset, needle in a haystack, training only with normal, Placebo test

Abstract

This article is a consideration on computer network intrusion detection using artificial neural networks, or whatever else using machine learning techniques. We assume an intrusion to a network is like a needle in a haystack not like a family of iris flower, and we consider how an attack can be detected by an intelligent way, if any.

References

G. E. Hinton, and S. J. Nowlan. How Learning can Guide Evolution. Complex Systems, 1, 1987. pp. 495-502.

G. Castellano, and A. M. Fanelli. Fuzzy Inference and Rule Extraction using a Neural Network. Neural Network World Journal, Vol. 3. 2000. pp. 361- 371.

S. J. Stolfo, F. Wei, W. Lee, A. Prodromidis, and P. K. Chan. KDD Cup knowledge discovery and data mining competition. 1999. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

G. K. Kuchimanchi, V. V. Phoha, K. S. Balagani, and S. R. Gaddam. Dimension Reduction Using Feature Extraction Methods for Real-time Misuse Detection Systems. Proceedings of Workshop on Information Assurance and Security. 2004. pp. 1555-1563.

S. S. Joshi, and V. V. Phoha. Investigating Hidden Markov Models Capabilities in Anomaly Detection. Proceedings of the 43rd ACM Southeast Conference, Vol. 1. 2005. pp. 99-103.

Z. Pan, H. Lian, G. Hu, and G. Ni. An Integrated Model of Intrusion Detection Based on Neural Network and Expert System. Proceedings of IEEE International Conference on Tools with Artificial Intelligence. 2005. pp. 671-672.

Z. Pan, S. Chen, G. Hu, and D. Zhangn. Hybrid Neural Network and C4.5 for Misuse Detection. Proceedings of International Conference on Machine Learning and Cybernetics. 2003. pp. 2463-2467.

T. Stibor, J. Timmis, and C. Eckert. A comparative Study of Real-valued Negative Selection to Statistical Anomaly Detection Techniques. Proceedings of International Conference on Artificial Immune Systems, Lecture Notes in Computer Science, Vol. 3627, Springer. 2005. pp. 262-275.

Z. Ji, and D. Dasgupta. Real-valued Negative Selection Algorithm with Variable-sized Detectors. Proceedings of Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science Vol. 3102, Springer. 2004. pp. 287-298.

H. H. Dam, K. Shafi, and H. A. Abbass. Can Evolutionary Computation Handle Large Dataset? Technical Report: The Artificial Life and Adaptive Robotics Laboratory, TR-ALAR-200507011. 2005.

M. Sabhnani, and G. Serpen. Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context. Proceedings of the International Conference on Machine Learning: Models, Technologies and Applications. 2003. pp. 209-215.

R. Shipman, M. Shackleton, and I. Harvey. The Use of Neutral Genotype-phenotype Mappings for Improved Evolutionary Search. BT Technology Journal, Vol. 18, No. 4, 2000. pp. 103-111.

Tina Yu and J. Miller. Finding Needles in Haystacks is Not Hard with Neutrality. Proceedings of EuroGP-2002, Lecture Notes in Computer Science Vol. 2278, Springer. 2002. pp. 13-25.

M. Collins. Finding Needles in Haystacks is Harder with Neutrality. Proceedings of Genetic and Evolutionary Computation Conference. 2005. pp. 1613-1618.

M. Collins. Counting Solutions in Reduced Boolean Parity. CD-ROM of Workshop Proceedings in Genetic and Evolutionary Computation Conference. 2004.

P. Laskov, P. Dussel, C. Schafer, and K. Rieck. Learning Intrusion Detection: Supervised or Unsupervised? Proceedings of International Conference on Image Analysis and Processing. Lecture Notes in Computer Science, Vol. 3617, Springer. 2005. pp. 50-57.

J. Gomez, F. Gonzalez, and D. Dasgupta. An Immuno-Fuzzy Approach to Anomaly Detection. Proceedings of IEEE International Conference on Fuzzy Systems, Vol. 2. 2003. pp. 1219-1224.

S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri. Self Nonself Discrimination in a Computer. Proceedings of IEEE Symposium on Research in Security and Privacy. 1994. pp. 202-212.

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Published

2014-08-01

How to Cite

Imada, A. (2014). HOW MANY PARACHUTISTS WILL BE NECESSARY TO FIND A NEEDLE IN A PASTORAL - WHO IS A LUCKY ONE?. International Journal of Computing, 5(3), 126-134. https://doi.org/10.47839/ijc.5.3.417

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Articles