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Research on data mining of permissions mode for Android malware detection

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Abstract

Android system uses a permission mechanism to allow users and developers to regulate access to private information and system resources required by Android applications (apps). Permissions can be behaviors and characteristics of an app, and widely used by Android malware detection. This paper designs a novel method to extract contrasting permission patterns for comparing the differences between Android benign apps and malware based on permissions, and use these differences to detect Android malware. Unlike existing works, this work first analyzes required and used permission. Then use support-based permission candidate method to mining unique required or used permission patterns, and use these patterns to detect Android malware. In experiment, this approach uses permission patterns from Android malware to detect a mixed Android app dataset. The results show that the proposed method can achieve 94% accuracy, 5% false positive, and 1% false negative.

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References

  1. Zhou, J., Dong, H., Feng, J.: Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates. Appl. Math. Comput. 293(C), 617–629 (2017)

    MathSciNet  MATH  Google Scholar 

  2. Gilbert, P., Byung-Gon, C., Landon, P.C., Jaeyeon, J.: Vision: automated security validation of mobile apps at app markets. 2001 International Workshop on Mobile Cloud Computing and Services, pp. 21–26. ACM, New York (2011)

  3. Frank, M., Dong, B., Felt, A.P., Song, D.: Mining permission request patterns from Android and Facebook applications. 2012 IEEE International Conference on Data Mining, pp. 870–875. IEEE Computer Society, Washington (2012)

  4. Felt, A.P., Ha, E., Egelman, S., Haney, A., Chin, E., Wagner, D.: Android permissions: user attention, comprehension and behavior. 2012 Symposium on Usable Privacy and Security, pp. 1–14. ACM, New York (2012)

  5. Zhou, Y., Jiang, X.: Dissecting, Android malware: characterization and evolution. 2012 IEEE Symposium on Security and Privacy, pp. 95–109. IEEE Computer Society, Washington (2012)

  6. Tchakounte, F.: Permission-based malware detection mechanisms on android: analysis and perspectives. J. Comput. Sci. Softw. Appl. 1(2), 63–77 (2014)

    Google Scholar 

  7. Liang, S., Du, X.: Permission-combination-based scheme for Android mobile malware detection. ICC 2014: IEEE International Conference on Communications Washington, pp. 2301-2306. IEEE Computer Society (2014)

  8. Feldman, S., Stadther, D., Wang, B.: Manilyzer: automated android malware detection through manifest analysis. MASS 2014: IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, pp. 767–772. IEEE Computer Society, Washington (2014)

  9. Rovelli, P., Vigfusson, Y.: PMDS: permission-based malware detection system. Inf. Syst. Security Lect. Notes Comput. Sci. 8880, 338–357 (2014)

    Google Scholar 

  10. Gong, H., Li, J., Xu, C.: Local well-posedness of strong solutions to density-dependent liquid crystal system. Nonlinear Anal. Theory Methods Appl. 147, 26–44 (2016)

    MathSciNet  MATH  Google Scholar 

  11. Li, X., Jingna, L., Qiang, L.: Existence of weak solutions for some singular parabolic equation. Acta Math. Scientia 36(6), 1651–1661 (2016)

    MathSciNet  MATH  Google Scholar 

  12. Zhang, J., Feng, Z., Xu, P., Liang, H.: Generalized varying coefficient partially linear measurement errors models. Ann. Inst. Stat. Math. 69(1), 97–120 (2017)

    MathSciNet  MATH  Google Scholar 

  13. Kong, D., Li, W., Zou, Y.: On small bases which admit points with two expansions. J. Number Theory 173, 100–128 (2017)

    MathSciNet  MATH  Google Scholar 

  14. Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., Drebin, R.K.: Effective and explainable detection of Android malware in your pocket. Network and Distributed System Security Symposium, pp. 199–210. IEEE Computer Society, Washington (2014)

  15. Yao, H., Xiong, M., et al.: Mining multiple spatial–temporal paths from social media data. Future Generation Computer Systems, online

  16. Liu, S., Young, S.D.: A survey of social media data analysis for physical activity surveillance. J. Forensic Legal Med., Online

  17. Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. Online

  18. Injadat, M.N., Salo, F., Nassif, A.B.: Data mining techniques in social media: a survey. Neurocomputing 214, 654–670 (2016)

    Google Scholar 

  19. Shao, H., Zhang, Y., Li, W.: Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 65, 66–78 (2017)

    Google Scholar 

  20. Brandt, T., Bendler, J., Neumann, D.: Social media analytics and value creation in urban smart tourism ecosystems. Inf. Manag. 54, 703–713 (2017)

    Google Scholar 

  21. Cui, W., Wang, P.: An algorithm for event detection based on social media data. Neurocomputing 254, 53–58 (2017)

    Google Scholar 

  22. Singh, A., Shukl, N., et al.: etc. Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E. online

  23. Qiang, W., Zhi, J., Yan, X.: Service discovery for internet of things based on probabilistic topic model. J. Softw. 25(8), 1640–1658 (2014)

    Google Scholar 

  24. Zhihong, Q., Yiju, W.: loT technology and application. ACTA Electronica Sinica 40(5), 1023–1029 (2012)

    Google Scholar 

  25. Yunquan, G., Xiaoyong, L., Binxing, F.: Survey on the search of Internet of things. J. Commun. 36(12), 57–76 (2015)

    Google Scholar 

  26. Haiming, C., Li, C., Kaibin, X.: A comparative study on architectures and implementation methodologies of internet of things. Chin. J. Comput. 36(1), 168–188 (2013)

    Google Scholar 

  27. Haiming, C., Li, C., Kaibin, X.: Information sensing and interaction technology in internet of things. Chin. J. Comput. 35(6), 1147–1163 (2012)

    Google Scholar 

  28. Qinyan, M., Shubin, S.: Information model and capability analysis of Internet of things. J. Softw. 25(8), 1685–1695 (2014)

    Google Scholar 

  29. l-Fuqaha, A., Guizani, M., et al.: Internet of things: a survey on enabling technologies, protocols and applications. IEEE Commun. Surv. Tutor. https://doi.org/10.1109/COMST.2015.2444095 (2015)

    Google Scholar 

  30. Nan, J., Liang, Y., et al.: A novel exercise thermophysiology comfort prediction model with fuzzy logic. Mob. Inf. Syst. https://doi.org/10.1155/2016/8586493 (2016)

    Google Scholar 

  31. Qingzhen, X., Susu, B., et al.: Mx/G/1 queue with multiple vacations. Stoch. Anal. Appl. 25(1), 127–140 (2007)

    MathSciNet  MATH  Google Scholar 

  32. Bo, C., Wen-Sheng, C.: Noisy image segmentation based on wavelet transform and active contour model. Appl. Anal. 90(8), 1243–1255 (2011)

    MathSciNet  MATH  Google Scholar 

  33. Bo, C., Qing-Hua, Z., et al.: A novel adaptive partial differential equation model for image segmentation. Appl. Anal. 93(11), 2440–2450 (2012)

    MATH  Google Scholar 

  34. Xiuli, L., Zengqin, Z.: Iterative technique for a third-order differential equation with three-point nonlinear boundary value conditions. Electron. J. Qual. Theory Differ. Equ. 12(1), 1–10 (2016). https://doi.org/10.14232/ejqtde.2016.1.12

    Article  MathSciNet  MATH  Google Scholar 

  35. Qingzhen, X., Zhoutao, W., et al.: Thermal comfort research on human CT data modeling. Multimed. Tool. Appl. https://doi.org/10.1007/s11042-017-4537-9

    MathSciNet  Google Scholar 

  36. Yang, S., Hu, C.: Pure Weierstrass gaps from a quotient of the Hermitian curve. Finite Fields Appl. 50, 251–271 (2018)

    MathSciNet  MATH  Google Scholar 

  37. Peihe, W., Lingling, Z.: Some geometrical properties of convex level sets of minimal graph on 2-dimensional Riemannian manifolds. Nonlinear Anal. Theory Method Appl. 130(1), 1–13 (2016)

    MathSciNet  MATH  Google Scholar 

  38. Peihe, W., Dekai, Z.: Convexity of level sets of minimal graph on space form with nonnegative curvature. J. Differ. Equ. 262, 5534–5564 (2017)

    MathSciNet  MATH  Google Scholar 

  39. Fushan, L., Qingyong, G.: Blow-up of solution for a nonlinear Petrovsky type equation with memory. Appl. Math. Comput. 274, 383–392 (2016)

    MathSciNet  MATH  Google Scholar 

  40. Li, G., Zhang, Z., Wang, L., Pan, J., Chen, Q.: One-class collaborative filtering based on rating prediction and ranking prediction. Knowl. Based Syst. 124: 46-54 (2017)

    Google Scholar 

  41. Li, G., Ou, W.: Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing 204, 17–25 (2016)

    Google Scholar 

  42. Li, G., Wang, L., Li, Y.: Robust personalized ranking from implicit feedback. Int. J. Pattern Recognit. Artif. Intell. 30(1), 1659001:1-28 (2016)

    Google Scholar 

  43. Li, G., Chen, Q.: Exploiting explicit and implicit feedbacks for personalized ranking. Math. Probl. Eng. 2016, 1–11 (2016)

    MathSciNet  MATH  Google Scholar 

  44. Xu, R., Meng, F.: Some new weakly singular integral inequalities and their applications to fractional differential equations. J. Inequal. Appl. 2016(1), 1–16 (2016)

    MathSciNet  Google Scholar 

  45. Mao, A., Yang, L., et al.: Existence and concentration of solutions of Schroinger-Poisson system. Appl. Math. Lett. 68, 8–12 (2017)

    MathSciNet  Google Scholar 

  46. Li, P., Ren, G.: Some classes of equations of discrete type with harmonic singular operator and convolution. Appl. Math. Comput. 284, 185–194 (2016)

    MathSciNet  MATH  Google Scholar 

  47. Wang, B., Iserles, A., Wu, X.: Arbitrary order trigonometric fourier collocation methods for multi-frequency oscillatory systems. Found. Comput. Math. 16(1), 151–181 (2016)

    MathSciNet  MATH  Google Scholar 

  48. Liu, G., Xu, S., et al.: New insight into reachable set estimation for uncertain singular time-delay systems. Appl. Math. Comput. 320, 769–780 (2018)

    MathSciNet  MATH  Google Scholar 

  49. Mao, A., Chang, H.: Kirchhoff type problems in RN with radial potentials and locally Lipschitz functional. Appl. Math. Lett. 62, 49–54 (2016)

    MathSciNet  MATH  Google Scholar 

  50. Chen, Z.-M., Xiong, X.: Equilibrium states of the Charney-DeVore quasi-geostrophic equation in mid-latitude atmosphere. J. Math. Anal. Appl. 444(2), 1403–1416 (2016)

    MathSciNet  MATH  Google Scholar 

  51. Wang, B., Iserles, A., Wu, X.: Arbitrary order trigonometric Fourier collocation methods for second-order ODEs. Found. Comput. Math. 16, 151–181 (2016)

    MathSciNet  MATH  Google Scholar 

  52. Wang, B., Wu, X., Meng, F.: Trigonometric collocation methods based on Lagrange basis polynomials for multi-frequency oscillatory second order differential equations. J. Comput. Appl. Math. 313, 185–201 (2017)

    MathSciNet  MATH  Google Scholar 

  53. Wang, B., Yang, H., Meng, F.: Sixth order symplectic and symmetric explicit ERKN schemes for solving multi frequency oscillatory nonlinear Hamiltonian equations. Calcolo 54, 117–140 (2017)

    MathSciNet  MATH  Google Scholar 

  54. Yang, S., Hu, C.: Weierstrass semigroups from Kummer extensions. Finite Fields Appl. 45, 264–284 (2017)

    MathSciNet  MATH  Google Scholar 

  55. Yang, S., Yao, Z.-A., Zhao, C.-A.: The weight distributions of two classes of p-ary cyclic codes with few weights. Finite Fields Appl. 44, 76–91 (2017)

    MathSciNet  MATH  Google Scholar 

  56. Yang, S., Yao, Z.-A.: Complete weight enumerators of a class of linear codes. Dis-crete Math. 340, 729–739 (2017)

    MathSciNet  MATH  Google Scholar 

  57. Ma, X., Wang, P., Wei, W.: Constant mean curvature surfaces and mean curvature flow with non-zero Neumann boundary conditions on strictly convex domains. J. Funct. Anal. 274, 252–277 (2018)

    MathSciNet  MATH  Google Scholar 

  58. Yang, J., Li, J., Liu, S.: A novel technique applied to the economic investigation of recommender system. Multimed. Tools Appl. https://doi.org/10.1007/s11042-017-4752-4

    Google Scholar 

  59. Yang, J., Li, J., Liu, S.: A new algorithm of stock data mining in Internet of multimedia things. J. Supercomput. https://doi.org/10.1007/s11227-017-2195-3

  60. Zhang, Q.: Mathematical modeling and numerical study of carbonation in porous concrete materials. Appl. Math. Comput. 281, 16–27 (2016)

    MATH  Google Scholar 

  61. Zhang, J., Chen, Q., Zhou, N.: Correlation analysis with additive distortion measurement errors. J. Stat. Comput. Simul. 87(4), 664–688 (2016)

    MathSciNet  Google Scholar 

  62. Li, Y., Wang, R., Yao, N., Zhang, S.: A moderate deviation principle for stochastic Volterra equation. Stat. Probab. Lett. 122, 79–85 (2017)

    MathSciNet  MATH  Google Scholar 

  63. Tan, W., Ji, Y.: On the pullback attractor for the non-autonomous SIR equations with diffusion. J. Math. Anal. Appl. 449(2), 1850–1862 (2017)

    MathSciNet  MATH  Google Scholar 

  64. Tang, H., Wang, Y.: Quantitative versions of the joint distributions of Hecke eigenvalues. J. Number Theory 169, 295–314 (2016)

    MathSciNet  MATH  Google Scholar 

  65. Peng, X., Shang, Y., Zheng, X.: Lower bounds for the blow-up time to a nonlinear viscoelastic wave equation with strong damping. Appl. Math. Lett. 76, 66–73 (2018)

    MathSciNet  MATH  Google Scholar 

  66. Li, F., Li, J.: Global existence and blow-up phenomena for p-Laplacian heat equation with inhomogeneous Neumann boundary conditions. Bound. Value Probl. 2014, 219 (2014)

    MathSciNet  MATH  Google Scholar 

  67. Li, F., Li, J.: Global existence and blow-up phenomena for nonlinear divergence form parabolic equations with inhomogeneous Neumann boundary conditions. J. Math. Anal. Appl. 385, 1005–1014 (2012)

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The Project was supported by the National Natural Science Foundation of China (No. 61402185), Science Foundation of Guangdong Provincial Communications Department (grant number 2015-02-064), Natural Science Foundation of Guangdong Province (No. 2015A030313382), Guangdong Provincial Public Research and Capacity Building Foundation funded project (No. 2015A020217011 & 2016A020223012), STPF of University in Shandong Province of China (J17KA161), and South China Normal University–Bluedon Information Security Technologies Co., Ltd joint laboratory project LD20170201.

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Authors and Affiliations

  1. Department of Information Engineering, Guangzhou Huashang vocational college, Guangzhou, 511300, China

    Chao Wang & Qingzhen Xu

  2. School of Mathematical Sciences, Qufu Normal University, Qufu, 273165, China

    Xiuli Lin

  3. School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou, 510631, China

    Shouqiang Liu

Authors
  1. Chao Wang
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  2. Qingzhen Xu
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  3. Xiuli Lin
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  4. Shouqiang Liu
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Correspondence to Qingzhen Xu.

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Wang, C., Xu, Q., Lin, X. et al. Research on data mining of permissions mode for Android malware detection. Cluster Comput 22 (Suppl 6), 13337–13350 (2019). https://doi.org/10.1007/s10586-018-1904-x

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