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Android malware detection using feature fusion and artificial data
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and System science.
2018 (English)In: Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018, 2018, p. 702-709, article id 8511966Conference paper, Published paper (Refereed)
Abstract [en]

For the Android malware detection / classification anti-malware community has relied on traditional malware detection methods as a countermeasure. However, traditional detection methods are developed for detecting the computer malware, which is different from Android malware in structure and characteristics. Thus, they may not be useful for Android malware detection. Moreover, majority of suggested detection approaches may not be generalized and are incapable of detecting zero-day malware due to different reasons such as available data set with specific set of examples. Thus, their detection accuracy may be questionable. To address this problem, this paper presents a malware classification approach with a reliable detection accuracy and evaluate the approach using artificially generated examples. The suggested approach generates the signature profiles and behavior profiles of each application in the data set, which are further used as input for the classification task. For improving the detection accuracy, feature fusion of features from filter methods and wrapper method and algorithm fusion is investigated. Without affecting the detection accuracy, the optimal balance between real world examples and synthetic examples is also investigated. The experimental results suggest that both AUC and F1 can be obtained up to 0.94 for both known and unknown malware using original examples and synthetic examples.

Place, publisher, year, edition, pages
2018. p. 702-709, article id 8511966
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:miun:diva-35144DOI: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00123ISI: 000450146600108Scopus ID: 2-s2.0-85056882366OAI: oai:DiVA.org:miun-35144DiVA, id: diva2:1269474
Conference
16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018; Athens; Greece; 12 August 2018 through 15 August 2018
Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2018-12-11Bibliographically approved

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Shahzad, Raja Khurram

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • nn-NO
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Output format
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  • asciidoc
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