Behavior Intention Derivation of Android Malware Using Ontology Inference
Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/9250297 |
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| _version_ | 1832555170806890496 |
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| author | Jian Jiao Qiyuan Liu Xin Chen Hongsheng Cao |
| author_facet | Jian Jiao Qiyuan Liu Xin Chen Hongsheng Cao |
| author_sort | Jian Jiao |
| collection | DOAJ |
| description | Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system. |
| format | Article |
| id | doaj-art-29d3b818d6644d44915f9cd014fdc54b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-29d3b818d6644d44915f9cd014fdc54b2025-02-03T05:49:27ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552018-01-01201810.1155/2018/92502979250297Behavior Intention Derivation of Android Malware Using Ontology InferenceJian Jiao0Qiyuan Liu1Xin Chen2Hongsheng Cao3Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, ChinaBeijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, ChinaSchool of Computer Science, Beijing Information Science and Technology University, Beijing, ChinaBeijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, ChinaPrevious researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.http://dx.doi.org/10.1155/2018/9250297 |
| spellingShingle | Jian Jiao Qiyuan Liu Xin Chen Hongsheng Cao Behavior Intention Derivation of Android Malware Using Ontology Inference Journal of Electrical and Computer Engineering |
| title | Behavior Intention Derivation of Android Malware Using Ontology Inference |
| title_full | Behavior Intention Derivation of Android Malware Using Ontology Inference |
| title_fullStr | Behavior Intention Derivation of Android Malware Using Ontology Inference |
| title_full_unstemmed | Behavior Intention Derivation of Android Malware Using Ontology Inference |
| title_short | Behavior Intention Derivation of Android Malware Using Ontology Inference |
| title_sort | behavior intention derivation of android malware using ontology inference |
| url | http://dx.doi.org/10.1155/2018/9250297 |
| work_keys_str_mv | AT jianjiao behaviorintentionderivationofandroidmalwareusingontologyinference AT qiyuanliu behaviorintentionderivationofandroidmalwareusingontologyinference AT xinchen behaviorintentionderivationofandroidmalwareusingontologyinference AT hongshengcao behaviorintentionderivationofandroidmalwareusingontologyinference |