permission behavior based malware di gupta neha (6 risultati)

Paperback. Condizione: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock.

Taschenbuch. Condizione: Neu. Neuware -This malware detection system detects the malware using static and dynamic features of the android apps. The static feature used for this purpose is permissions and the dynamic feature is the frequency of system calls. The machine learning algorithms are applied on the extracted features and a model is devised which identifies the Android benign applications and the Android malicious applications.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch.

Taschenbuch. Condizione: Neu. Permission and Behavior Based Malware Detection of Android Apps | Neha Gupta (u. a.) | Taschenbuch | 68 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330322639 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This malware detection system detects the malware using static and dynamic features of the android apps. The static feature used for this purpose is permissions and the dynamic feature is the frequency of system calls. The machine learning algorithms are applied on the extracted features and a model is devised which identifies the Android benign applications and the Android malicious applications. 68 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta NehaNeha Gupta is doing M. Tech. (Computer Science and Engineering) from The NorthCap University, Gurugram. She received her MCA degree from YMCA Institute of Engineering, Faridabad in 2007.This malware detection system det.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This malware detection system detects the malware using static and dynamic features of the android apps. The static feature used for this purpose is permissions and the dynamic feature is the frequency of system calls. The machine learning algorithms are applied on the extracted features and a model is devised which identifies the Android benign applications and the Android malicious applications.