Abstract
With the volume of data required to be analysed and interpreted by security analysts, the possibility of human error looms large and the consequences possibly harmful for some systems in the event of an adverse event not being detected. In this paper we suggest machine learning algorithms that can assist in supporting the security function effectively and present a framework that can be used to choose the best algorithm for a specific domain. A qualitative framework was produced, and it is suggested that a naive Bayesian classifier and artificial neural network based algorithms are most likely the best candidates for the proposed application. A testing framework is proposed to conduct a quantitative evaluation of the algorithms as the next step in the determination of best fit for purpose algorithm. Future research will look to repeat this process for cyber security specific applications, and also examine GPGPU optimisations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ahmed, F.E.: Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular Cancer 4(29), 12 (2005)
Zhou, Z.-H., Jiang, Y., et al.: Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 24(1), 25–36 (2002)
O’Halloran, M., Mcginley, B., Conceição, R.C., Morgan, F., Jones, E., Glavin, M.: Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast. Progress In Electromagnetics Research 113(16), 413–428 (2011)
Ahmad, A.M., Khan, G.M., et al.: Breast cancer detection using cartesian genetic programming evolved artificial neural networks. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, pp. 1031–1038. ACM, Philadelphia (2012)
Muthukkumarasamy, V., Blumenstein, M.M., et al.: Intelligent Illicit Object Detection System for Enhanced Aviation Security. Korea Advanced Institute of Science and Technology (2004)
Singh, M., Singh, S., et al.: A knowledge-based framework for image enhancement in aviation security. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(6), 2354–2365 (2004)
Guzella, T.S., Caminhas, W.M.: A review of machine learning approaches to Spam filtering. Expert Systems with Applications 36(7), 10206–10222 (2009)
Huadong, W., Siegel, M., Khosla, P.: Vehicle sound signature recognition by frequency vector principal component analysis. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference, IMTC 1998 (1998)
Krishnamoorthy, P., Kumar, S.: Hierarchical audio content classification system using an optimal feature selection algorithm. Multimed. Tools Appl. 54, 415–444 (2011)
Sivanandam, S.N., Deepa, S.N.: An Introduction to Genetic Algorithms. Springer, Berlin (2008)
Wooldridge, M.J.: An Introduction to Multiagent Systems. John Wiley & Sons, Chichester (2002)
Graupe, D.: Principles of Artificial Neural Networks, 2nd edn. World Scientific, Singapore (2007)
Bayes, M., Price, M.: An Essay towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S. Philosophical Transactions 53, 370–418 (1763)
Rumelhart, D.E., McClelland, J.L.: Parallel distributed processing: Exploration in the microstructure of cognition. MIT Press, Cambridge (1986)
Cantú-Paz, E., Kamath, C.: An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Trans. Syst. Man Cybern. B Cybern. 35(5), 915–927 (2005)
Sukanesh, R., Harikumar, R.: A Comparison of Genetic Algorithm & Neural Network (MLP) In Patient Specific Classification of Epilepsy Risk Levels from EEG Signals. Engineering Letters 14, 1 (2007)
Zheng, F., Webb, G.I.: A Comparative Study of Semi-naive Bayes Methods in Classification Learning. In: Simoff, S.J., Williams, G.J., Galloway, J., Kolyshkina, I. (eds.) Proceedings of the Fourth Australasian Data Mining Workshop (AusDM 2005), pp. 141–156. University of Technology, Sydney (2005)
Fleizach, C., Fukushima, S.: A naive Bayes classifier on 1998 KDD Cup (2006)
Dekhtyar, M., Dikovsky, A., Valiev, M.: Complexity of Multi-agent Systems Behavior. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 125–136. Springer, Heidelberg (2002)
Elkind, E., Markakis, E.: Computational Complexity in Multi-Agent Systems. In: 10th European Agent Systems Summer School, EASSS 2008 (2008)
Owechko, Y., Shams, S.: Comparison of neural network and genetic algorithms for a resource allocation problem. In: Proc. Neural Networks, IEEE World Congress on Computational Intelligence (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Johnstone, M.N., Woodward, A. (2012). Towards Effective Algorithms for Intelligent Defense Systems. In: Xiang, Y., Lopez, J., Kuo, CC.J., Zhou, W. (eds) Cyberspace Safety and Security. CSS 2012. Lecture Notes in Computer Science, vol 7672. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35362-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-35362-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35361-1
Online ISBN: 978-3-642-35362-8
eBook Packages: Computer ScienceComputer Science (R0)