Abstract
In this paper, we proposed a new approach using bio-inspired algorithms such as Clonal Selection Algorithm (CLONALG) and Artificial Neural Networks (ANNs) which aims to handle virus detection problem. The point of difference is using ANNs as the detectors and CLONALG as the algorithm for finding the best ANN’s structure and weights. According to experimental results, the proposed model has an acceptable detection rate and false positive rate.
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Nguyen, V.T., Anh, N.P., Khang, M.T., Ngan, N.H., Thai, N.Q., Quoc, N.T. (2014). A Combination of Clonal Selection Algorithm and Artificial Neural Networks for Virus Detection. In: Jeong, H., S. Obaidat, M., Yen, N., Park, J. (eds) Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41674-3_15
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DOI: https://doi.org/10.1007/978-3-642-41674-3_15
Publisher Name: Springer, Berlin, Heidelberg
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