Generating a rich mobile dataset using a central management approach / Khelwa Fariza Mohd Sakroni
This project will brief reader with a comprehensive study on mobile Botnets. Bots are small-size malwares that infect computers or mobile network, which can join with other bots via the Internet to form a network of bots called Botnet. Botnets and their bots have a dynamic and flexible nature. The B...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | https://ir.uitm.edu.my/id/eprint/80530/1/80530.pdf |
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| Summary: | This project will brief reader with a comprehensive study on mobile Botnets. Bots are small-size malwares that infect computers or mobile network, which can join with other bots via the Internet to form a network of bots called Botnet. Botnets and their bots have a dynamic and flexible nature. The Botmasters, who control the Botnets, update the bots and change their codes day by day to avoid the traditional detection methods such as signature-based anti-viruses. Mobile environment is less protected and Botmasters have taken advantage of the lack of security knowledge of mobile users in an attempt to steal private data and earn money illegally. In addition, many techniques are employed by Botmasters to make their Botnets undetectable for as long as possible. This primary purpose of this project is to presents a method to generate and produce rich mobile datasets for mobile security researchers. The approaches used to achieve this project are through literature studies of mobile Botnets, Botnets detections and mobile data collection software which run on the background of the mobile phones. Project development is carried out using Google Application, named Packet Capture which been installed in the Android Smartphone and the collected mobile data been analyzed using a network protocol software, named Wireshark. The project result are expected that the propose method runs in the mobile Smartphone is able to be generate and capture the valid mobile dataset and these dataset able to analyze and evaluate as a rich dataset for future mobile security study. |
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