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Fast virus detection by using high speed time delay neural networks

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Abstract

This paper presents an intelligent approach to detect unknown malicious codes by using new high speed time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Systems, Mansoura University, Mansoura, Egypt

    Hazem M. El-Bakry

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  1. Hazem M. El-Bakry
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Correspondence to Hazem M. El-Bakry.

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El-Bakry, H.M. Fast virus detection by using high speed time delay neural networks. J Comput Virol 6, 115–122 (2010). https://doi.org/10.1007/s11416-009-0120-x

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  • DOI: https://doi.org/10.1007/s11416-009-0120-x

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