In recent years,Android applications have caused personal privacy leaks frequently.In order to analyze the malicious behavior,taint analysis technology can be used to track the API call chain,build a control-flow grap...In recent years,Android applications have caused personal privacy leaks frequently.In order to analyze the malicious behavior,taint analysis technology can be used to track the API call chain,build a control-flow graph of function,and determine whether there is a security risk.However,with the continuous escalation of offensive and defensive confrontation of source code,more and more applications use reinforcement technology to prevent security practitioners from performing reverse analysis,therefore it is impossible to analyze function-behavior from the source code.Thus,we design a framework of taint analysis that applied to the Android applications,which automatically unpacks the Android APKs,restores the real source code of the App,performs taint analysis,and generates a control-flow graph of function.Experimental tests showed that the system can cope with the current mainstream reinforcement technology and restore the real Dex file quickly.Simultaneously,compared with the number of nodes before packing,the generated control-flow graph had an explosive increase,which effectively assisted manual analysis of App with the privacy leakage behaviors.展开更多
Powered by the Internet and the ever-increasing level of informatization, the cyberspace has become increasingly complex and its security situation has become increasingly grim, which requires new adaptive and collabo...Powered by the Internet and the ever-increasing level of informatization, the cyberspace has become increasingly complex and its security situation has become increasingly grim, which requires new adaptive and collaborative defense technologies. In this paper, we introduced an extended interactive multi-agent decision model for decentralized cyber defense. Based on the significant advantages of the cooperative multi-agent decision-making, the decentralized interactive decision model DI-MDPs and the corresponding interaction and retrieval algorithms are proposed. Then, we analyzed the interactive decision by the calculation and update processes of three matrices, the stability and evolutionary equilibrium of the proposed model are also analyzed. Finally, we evaluated the performance of the proposed algorithms based on open data sets and standard test environments, the experimental results shown that the proposed work will be more applicable in cyber defense.展开更多
基金supported by Beijing Natural Science Foundation(No.4214061)。
文摘In recent years,Android applications have caused personal privacy leaks frequently.In order to analyze the malicious behavior,taint analysis technology can be used to track the API call chain,build a control-flow graph of function,and determine whether there is a security risk.However,with the continuous escalation of offensive and defensive confrontation of source code,more and more applications use reinforcement technology to prevent security practitioners from performing reverse analysis,therefore it is impossible to analyze function-behavior from the source code.Thus,we design a framework of taint analysis that applied to the Android applications,which automatically unpacks the Android APKs,restores the real source code of the App,performs taint analysis,and generates a control-flow graph of function.Experimental tests showed that the system can cope with the current mainstream reinforcement technology and restore the real Dex file quickly.Simultaneously,compared with the number of nodes before packing,the generated control-flow graph had an explosive increase,which effectively assisted manual analysis of App with the privacy leakage behaviors.
基金financially supported by the National Natural Science Foundation of China (No. 62106060)in part by the Beijing Natural Science Foundation (No. 4214061)
文摘Powered by the Internet and the ever-increasing level of informatization, the cyberspace has become increasingly complex and its security situation has become increasingly grim, which requires new adaptive and collaborative defense technologies. In this paper, we introduced an extended interactive multi-agent decision model for decentralized cyber defense. Based on the significant advantages of the cooperative multi-agent decision-making, the decentralized interactive decision model DI-MDPs and the corresponding interaction and retrieval algorithms are proposed. Then, we analyzed the interactive decision by the calculation and update processes of three matrices, the stability and evolutionary equilibrium of the proposed model are also analyzed. Finally, we evaluated the performance of the proposed algorithms based on open data sets and standard test environments, the experimental results shown that the proposed work will be more applicable in cyber defense.
