Abstract
This paper explores recent works in the application of artificial neural network (ANN) for security – namely, network security via intrusion detection systems, and authentication systems. This paper highlights a variety of approaches that have been adopted in these two distinct areas of study. In the application of intrusion detection systems, ANN has been found to be more effective in detecting known attacks over rule-based system; however, only moderate success has been achieved in detecting unknown attacks. For authentication systems, the use of ANN has evolved considerably with hybrid models being developed in recent years. Hybrid ANN, combining different variants of ANN or combining ANN with non-AI techniques, has yielded encouraging results in lowering training time and increasing accuracy. Results suggest that the future of ANN in the deployment of a secure environment may lie in the development of hybrid models that are responsive for real-world applications.
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Ling, M.H., Hassan, W.H. (2010). Harnessing ANN for a Secure Environment. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_67
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DOI: https://doi.org/10.1007/978-3-642-13318-3_67
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