DocumentCode :
3582134
Title :
Mobile keylogger detection using machine learning technique
Author :
Gunalakshmii, S. ; Ezhumalai, P.
Author_Institution :
Dept. of Comput. Sci. & Eng., Anna Univ., Chennai, India
fYear :
2014
Firstpage :
51
Lastpage :
56
Abstract :
Keylogger, a tool intended to record every keystroke made on the machine and offers the attacker the ability to steal large amounts of sensitive information without the permission of the owner of the message. The primary objective of this project is to detect keylogger applications and prevent data loss and sensitive information leakage. This project aims to identify the set of permissions and storage levels owned by each of the applications and hence differentiate applications with proper permissions and keylogger applications that can abuse permissions. The keyloggers are detected using Black-box technique. Black-box approach is based on behavioral characteristics which can be applied to all keyloggers and it does not rely on the structural characteristics of the keylogger. This project aims to develop detection system on mobile phones based on machine learning algorithm to detect keylogger applications.
Keywords :
data protection; learning (artificial intelligence); mobile computing; security of data; black-box technique; data loss prevention; machine learning algorithm; mobile keylogger detection; sensitive information leakage prevention; Androids; Conferences; Humanoid robots; Malware; Mobile communication; Mobile handsets; Support vector machines; Black-box; Keylogger; Smartphone; Spyware; machine learning; malware;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Communication and Systems, 2014 International Conference on
Print_ISBN :
978-1-4799-3671-7
Type :
conf
DOI :
10.1109/ICCCS.2014.7068167
Filename :
7068167
Link To Document :
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