Ngubiri john (10 risultati)

Condizione: New.

Condizione: New.

Paperback. Condizione: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Pison AsiimweI am PhD candidate of Computing and Management of Mbarara University of Science and Technology and UTAMU,holds a Master of Science in Computer Science of Makerere University.I teach at UCU-Kabale campus in the department.…

Condizione: New. Print on Demand.

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 the use of ICT Systems, Allan h. …

Condizione: New. PRINT ON DEMAND.

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.…