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Smartphone Malware Detection Model Based on Artificial Immune System

China Communications 2014年第A01期11卷 86-92页

作者： WU Bin LU Tianliang ZHENG Kangfeng ZHANG Dongmei LIN Xing informationsecuritylaboratory NationalDisasterRecoveryTechnologyEngineeringLaboratoryBeijingUniversityofPostsandTelecommunicationsBeijing100876P.R.China People'sPublicSecurityUniversityofChina Beijing100038P.R.China

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

关键词： artificial immune system smartphonemalware detection negative selection clonalselection

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Differentially Private Multidimensional Data Publication

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China Communications 2014年第A01期11卷 79-85页

作者： ZHANG Ji DONG Xin YU Jiadi LUO Yuan LI Minglu WU Bin SchoolofMathematicalSciences FudanUniversityShanghai201203China DepartmentofComputerScienceandEngineering ShanghaiJiaoTongUniversityShanghai200240China informationsecuritylaboratory NationalDisasterRecoveryTechnologyEngineeringLa-boratoryBeijingUniversityofPostaandTelecommunicationBeijing100876China

Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.

关键词： data publication differential privacy data utility

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