Artificial Intelligence: Design and Implementation of Entropy Based Artificially Immune Malware Detection System - Brossura
Sinossi
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.
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L'autore
Graduated as a Computer Engineer from National University of Sciences and Technology, Pakistan in 2010. This research based project was my final year project which I completed with Abdul Haseeb and Muhammad Bilal Bhatti. Since graduating, I have been doing research in the field of Artificial Intelligence(AI) and also plan to do Masters in AI.
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Artificial Intelligence
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LAP LAMBERT Academic Publishing Aug 2011, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 76 pp. Englisch. Codice articolo 9783845429991
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Artificial Intelligence
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LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ali MuhammadGraduated as a Computer Engineer from National University of Sciences and Technology, Pakistan in 2010. This research based project was my final year project which I completed with Abdul Haseeb and Muhammad Bilal Bhatti. . Codice articolo 5482098
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Artificial Intelligence
Editore:
LAP LAMBERT Academic Publishing Aug 2011, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Codice articolo 9783845429991
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Artificial Intelligence : Design and Implementation of Entropy Based Artificially Immune Malware Detection System
Editore:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783845429991
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Artificial Intelligence | Design and Implementation of Entropy Based Artificially Immune Malware Detection System
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LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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Taschenbuch. Condizione: 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. Codice articolo 106864461
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Artificial Intelligence: Design and Implementation of Entropy Based Artificially Immune Malware Detection System
Editore:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845429992
ISBN 13: 9783845429991
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paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Codice articolo ERICA82938454299926
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