Artificial intelligence design implementation de ali muhammad (3 resultados)

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  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2011

    3845429992 / 9783845429991

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    Librería: preigu, Osnabrück, Alemaniapreigu

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    EUR 43,40

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    Se envía de Alemania a Estados Unidos de America

    Cantidad disponible: 5 disponibles

    Taschenbuch. Condición: Neu. Artificial Intelligence | Design and Implementation of Entropy Based Artificially Immune Malware Detection System | Muhammad Ali (u. a.) | Taschenbuch | 76 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845429991 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2011

    3845429992 / 9783845429991

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    Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books

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    EUR 150,57

    Envío por EUR 29,47 
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    Cantidad disponible: 1 disponible

    paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2011

    3845429992 / 9783845429991

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    EUR 49,00

    Envío por EUR 60,66 
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    Cantidad disponible: 1 disponible

    Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many Malware detection systems these days are using signature based techniques to detect malwares and viruses. The zero day or new infected files are not detected by these signature based Anti Viruses and their signature is generated only after they have done their damage. Hence it becomes very important for a user to constantly update the antivirus software. To overcome these problems, we have proposed a solution based on Artificial Intelligence techniques. So the clients will not require frequent updates and probability of detecting zero day infections will rise abruptly. This project is based on implementing data mining algorithms mainly C4.5 Decision Tree learner. We have generated a dataset on the basis of already known malicious executable files. A C4.5 decision tree is generated based on the generated dataset and the unknown executables are passed through the tree to classify the executable as a malicious or a benign file. The purpose is to get rid of the manual signature based Malware detection systems that require constant updated signatures and making systems artificially immune to unknown and zero day malicious executables.…