9786139923069 - behavioral malware detection by data mining di ninyesiga, allan; ngubiri, john (9 risultati)
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.
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Da: preigu, Osnabrück, Germaniapreigu
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EUR 47,85
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Taschenbuch. Condizione: Neu. Behavioral Malware Detection by Data Mining | Allan Ninyesiga (u. a.) | Taschenbuch | 96 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139923069 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: p…reigu.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Malware cases are increasing both in numbers and fatality. Hackers design malware to compromise systems security mostly confidentiality, integrity, and availability. Malware elimination techniques exist but the malware must be dete…cted first. Malware detection techniques still have weaknesses of high false positive/negatives rates. The emergency of polymorphic malware has made the situation worse. Recent studies have shown data mining to be promising in identifying malware by analyzing API calls. However, in this approach, a file is detected as malicious or not. It is not classified on to which malware class it belongs. This makes its elimination harder as elimination schemes are mostly class based. Classification as a post detection process is important if the malware is to be eliminated from the system. We experiment on the use of data mining approach to classify malware using 4-gram API system calls. We use Windows Portable Executables (PE) with their corresponding API calls. Using the Cuckoo sandbox. Relevant 4-gram API call features are extracted using Term Frequency-Inverse Document Frequency(TF-IDF). Machine Learning algorithms are then applied to classify the malware. 96 pp. Englisch.
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Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
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Da: moluna, Greven, , Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ninyesiga AllanAllan Ninyesiga has obtained a Masters Degree in Computing with a Computer Security Specialization form Uganda Technology an Management University in 2017. Due to the broad increase in t…he use of ICT Systems, Allan h.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 54,90
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Malware cases are increasing both in numbers and fatality. Hackers design malware to compromise systems security mostly confidentiality, integrity, and availability. Malware elimination techniques exist but the malware must be detected… first. Malware detection techniques still have weaknesses of high false positive/negatives rates. The emergency of polymorphic malware has made the situation worse. Recent studies have shown data mining to be promising in identifying malware by analyzing API calls. However, in this approach, a file is detected as malicious or not. It is not classified on to which malware class it belongs. This makes its elimination harder as elimination schemes are mostly class based. Classification as a post detection process is important if the malware is to be eliminated from the system. We experiment on the use of data mining approach to classify malware using 4-gram API system calls. We use Windows Portable Executables (PE) with their corresponding API calls. Using the Cuckoo sandbox. Relevant 4-gram API call features are extracted using Term Frequency-Inverse Document Frequency(TF-IDF). Machine Learning algorithms are then applied to classify the malware.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 55,56
EUR 60,81 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Malware cases are increasing both in numbers and fatality. Hackers design malware to compromise systems security mostly confidentiality, integrity, and availability. Malware elimination techniques exist but the malware must be detected…first. Malware detection techniques still have weaknesses of high false positive/negatives rates. The emergency of polymorphic malware has made the situation worse. Recent studies have shown data mining to be promising in identifying malware by analyzing API calls. However, in this approach, a file is detected as malicious or not. It is not classified on to which malware class it belongs. This makes its elimination harder as elimination schemes are mostly class based. Classification as a post detection process is important if the malware is to be eliminated from the system. We experiment on the use of data mining approach to classify malware using 4-gram API system calls. We use Windows Portable Executables (PE) with their corresponding API calls. Using the Cuckoo sandbox. Relevant 4-gram API call features are extracted using Term Frequency-Inverse Document Frequency(TF-IDF). Machine Learning algorithms are then applied to classify the malware.
