The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 32-307
Full Name: Hazem El-Bakry
Position: Assistant Professor
Age: ON
Sex: Male
Address: P.O,Box 76
Country: EGYPT
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E-mail address: helbakry50@yahoo.com
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Title of the Paper: Fast Virus Detection by using High Speed Time Delay Neural Networks
Authors as they appear in the Paper: Hazem M. EL-Bakry and Nikos Mastorakis
Email addresses of all the authors:
Number of paper pages: 15
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.
Keywords: Fast Time Delay Neural Networks, Cross Correlation, Frequency Domain, Real and Complex Numbers, Virus Detection
EXTENSION of the file: .doc
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