International Workshop on Mobile Security

Research Article

Cost-Sensitive Detection of Malicious Applications in Mobile Devices

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  • @INPROCEEDINGS{10.1007/978-3-642-29336-8_27,
        author={Yael Weiss and Yuval Fledel and Yuval Elovici and Lior Rokach},
        title={Cost-Sensitive Detection of Malicious Applications in Mobile Devices},
        proceedings={International Workshop on Mobile Security},
        proceedings_a={MOBILE SECURITY},
        year={2012},
        month={10},
        keywords={Intrusion Detection Mobile Devices Malware Security Android sCost sensitive feature selection},
        doi={10.1007/978-3-642-29336-8_27}
    }
    
  • Yael Weiss
    Yuval Fledel
    Yuval Elovici
    Lior Rokach
    Year: 2012
    Cost-Sensitive Detection of Malicious Applications in Mobile Devices
    MOBILE SECURITY
    Springer
    DOI: 10.1007/978-3-642-29336-8_27
Yael Weiss,*, Yuval Fledel,*, Yuval Elovici,*, Lior Rokach,*
    *Contact email: wiessy@bgu.ac.il, fledely@bgu.ac.il, elovici@bgu.ac.il, liorrk@bgu.ac.il

    Abstract

    Mobile phones have become a primary communication device nowadays. In order to maintain proper functionality, various existing security solutions are being integrated into mobile devices. Some of the more sophisticated solutions, such as host-based intrusion detection systems (HIDS) are based on continuously monitoring many parameters in the device such as CPU and memory consumption. Since the continuous monitoring of many parameters consumes considerable computational resources it is necessary to reduce consumption in order to efficiently use HIDS. One way to achieve this is to collect less parameters by means of cost-sensitive feature selection techniques. In this study, we evaluate ProCASH, a new cost-sensitive feature selection algorithm which considers resources consumption, misclassification costs and feature grouping. ProCASH was evaluated on an Android-based mobile device. The data mining task was to distinguish between benign and malicious applications. The evaluation demonstrated the effectiveness of ProCASH compared to other cost sensitive algorithms.