A detection model of malware behaviors on android

Hang Dong; Qi Li; Feng Dong; Yong Peng; Guo Ai Xu

doi:10.13190/j.jbupt.2014.03.012

A detection model of malware behaviors on android

Hang Dong^*
, Qi Li
, Feng Dong
, Yong Peng
, Guo Ai Xu

^*Corresponding author for this work

Beijing University of Posts and Telecommunications
Information Technology Security Evaluation Center

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

A detection method was proposed to analyze the malicious behavior on Android, that combines hidden-Markov model (HMM) with support vector machine (SVM) for modeling as well as construct model for behaviors like networking and data accessing. This model takes advantage of both HMM and SVM and overcomes the shortcomings inside, and it is suitable for classification using dynamic behavior sequences. Experiments show that this method can capture the abnormal behaviors with high accuracy rate and lower false positive rate.

Original language	English
Pages (from-to)	58-61 and 88
Journal	Beijing Youdian Xueyuan Xuebao/Journal of Beijing University of Posts And Telecommunications
Volume	37
Issue number	3
DOIs	https://doi.org/10.13190/j.jbupt.2014.03.012
State	Published - 1 Jun 2014
Externally published	Yes

Keywords

Hidden-Markov model
Malware
Smartphone
Support vector machine

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10.13190/j.jbupt.2014.03.012

Cite this

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abstract = "A detection method was proposed to analyze the malicious behavior on Android, that combines hidden-Markov model (HMM) with support vector machine (SVM) for modeling as well as construct model for behaviors like networking and data accessing. This model takes advantage of both HMM and SVM and overcomes the shortcomings inside, and it is suitable for classification using dynamic behavior sequences. Experiments show that this method can capture the abnormal behaviors with high accuracy rate and lower false positive rate.",

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AB - A detection method was proposed to analyze the malicious behavior on Android, that combines hidden-Markov model (HMM) with support vector machine (SVM) for modeling as well as construct model for behaviors like networking and data accessing. This model takes advantage of both HMM and SVM and overcomes the shortcomings inside, and it is suitable for classification using dynamic behavior sequences. Experiments show that this method can capture the abnormal behaviors with high accuracy rate and lower false positive rate.

KW - Hidden-Markov model

KW - Malware

KW - Smartphone

KW - Support vector machine

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JF - Beijing Youdian Xueyuan Xuebao/Journal of Beijing University of Posts And Telecommunications

IS - 3

ER -

A detection model of malware behaviors on android

Abstract

Keywords

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