Malware Detection Using Logistic by Gupta Deepali (9 results)

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Paperback. Condition: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock.

Taschenbuch. Condition: Neu. Malware Detection By Using Logistic Regression With Dynamic Analysis | Deepali Gupta | Taschenbuch | 56 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200082640 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Use of mobile phones for getting to the web has extended broadly, anyway paying little heed to basic advances in processor power and exchange speed, using PDAs for examining isn't equivalent to work region scrutinizing. Content constituents and page arrangements are used to perform static goals to decide vindictiveness in the page content space. The investigation way centers on building an enhanced framework to distinguish malignant destinations consistently by joining of Dynamic recognizable proof methodologies. In the continuous separated from utilizing since quite a while ago settled static methodologies recommended already the dynamic examination bases on API Calls Observation and Filtration and furthermore for each malware family a plan of precedents are recognized, every model addressed a changed watched variation of the first malware record. The present work additionally centers on examining the executable page contents for malevolent code identification for API calls present in the content. Exploratory outcomes demonstrate that the enhanced strategy performs superior to past methodology that centers on just static examination. 56 pp. Englisch.

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta DeepaliMiss Deepali Gupta is research scholar perusing her M.Tech degree in computer science in Vishwavidyalaya Engineering College Ambikapur.Her interests include Artificial intelligence, Data Mining.Use of mobile phones f.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Use of mobile phones for getting to the web has extended broadly, anyway paying little heed to basic advances in processor power and exchange speed, using PDAs for examining isn't equivalent to work region scrutinizing. Content constituents and page arrangements are used to perform static goals to decide vindictiveness in the page content space. The investigation way centers on building an enhanced framework to distinguish malignant destinations consistently by joining of Dynamic recognizable proof methodologies. In the continuous separated from utilizing since quite a while ago settled static methodologies recommended already the dynamic examination bases on API Calls Observation and Filtration and furthermore for each malware family a plan of precedents are recognized, every model addressed a changed watched variation of the first malware record. The present work additionally centers on examining the executable page contents for malevolent code identification for API calls present in the content. Exploratory outcomes demonstrate that the enhanced strategy performs superior to past methodology that centers on just static examination.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Use of mobile phones for getting to the web has extended broadly, anyway paying little heed to basic advances in processor power and exchange speed, using PDAs for examining isn't equivalent to work region scrutinizing. Content constituents and page arrangements are used to perform static goals to decide vindictiveness in the page content space. The investigation way centers on building an enhanced framework to distinguish malignant destinations consistently by joining of Dynamic recognizable proof methodologies. In the continuous separated from utilizing since quite a while ago settled static methodologies recommended already the dynamic examination bases on API Calls Observation and Filtration and furthermore for each malware family a plan of precedents are recognized, every model addressed a changed watched variation of the first malware record. The present work additionally centers on examining the executable page contents for malevolent code identification for API calls present in the content. Exploratory outcomes demonstrate that the enhanced strategy performs superior to past methodology that centers on just static examination.