DocumentCode :
131783
Title :
A Gaussian mixture model for dynamic detection of abnormal behavior in smartphone applications
Author :
El Attar, Ali ; Khatoun, Rida ; Lemercier, Marc
Author_Institution :
ICD HETIC, Univ. of Technol. of Troyes, Troyes, France
fYear :
2014
fDate :
15-19 Sept. 2014
Firstpage :
1
Lastpage :
6
Abstract :
Nowadays smartphones get increasingly popular which also attracted hackers. With the increasing capabilities of such phones, more and more malicious softwares targeting these devices have been developed. Malwares can seriously damage an infected device within seconds. This paper focus on the aggregation of a popular probabilistic model: the Gaussian mixture model, for a dynamic detection of the abnormal behavior in smartphone applications. More precisely, we propose to apply a mixture model estimation technique on the behavior of applications, for density modeling and data clustering. The mixture models of the different smartphones are then aggregated to estimate the global model that reflecting the probability density of the global data set. Furthermore, we carry out a model-based clustering outlier detection to compute an anomaly score for each application, leading to identify the malware applications. Initial experiments results prove the efficiency and the accuracy of the model-based clustering in detecting abnormal applications with a low false alerts rate.
Keywords :
Gaussian processes; invasive software; mixture models; mobile computing; pattern clustering; smart phones; Gaussian mixture model; abnormal behavior dynamic detection; data clustering; density modeling; false alerts rate; global data set probability density; malware applications; mixture model estimation technique; model-based clustering outlier detection; smartphone applications; Computational modeling; Data models; Gaussian mixture model; Malware; Mathematical model; Measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Information Infrastructure and Networking Symposium (GIIS), 2014
Conference_Location :
Montreal, QC
Type :
conf
DOI :
10.1109/GIIS.2014.6934278
Filename :
6934278
Link To Document :
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