• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.9-14
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• 2014

In this paper, we propose AMV scheme which supports k-anonymization by using vectors for mobile clients. AMV can produces the minimal cloaking area using motion vector information of users (clients). The main reason for minimizing cloaking area is a server has to send the object information to all users who request the spatial queries. The experimental results show that the proposed AMV has superior performance over existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.