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A novel approach for mobile malware classification and detection in Android systems

  • Qingguo Zhou
  • Fang Feng
  • Zebang Shen
  • Rui Zhou
  • Meng-Yen Hsieh
  • Kuan-Ching Li
Article
  • 80 Downloads

Abstract

With the increasing number of malicious attacks, the way how to detect malicious Apps has drawn attention in mobile technology market. In this paper, we proposed a detection model to seek and track malware Apps actions in such devices. To characterize the behaviors of Apps, dynamic features of each App were constrained in 166-dimension and a novel machine learning classifier is employed to detect malware Apps, and alarm will be triggered if an Android-based App is detected as malicious. With such, we can avoid a detected malware spreading out in larger scale, affecting extensively our society. Detailed description of the detection model is provided, as well the core technologies of this novel machine learning classifier are presented. From experiments performed on a set of Android-based malware and benign Apps, we observe that the proposed classification algorithm achieves highest accuracy, true-positive rate, false-positive rate, precision, recall, f-measure in comparison to other methods as K-Nearest Neighbor (KNN), Naive Bayesian (NB), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision tree (DT), Linear Discriminant Analysis (LDA) and Back Propagation (BP). The proposed detection model is promising and can effectively be applied to Android malware detection, providing early detection and the prospect of warning users of threatens ahead.

Keywords

Security Mobile malware detection System call Innovative classification algorithm Dynamic analysis 

Notes

Acknowledgements

This work was supported by Ministry of Education - China Mobile Research Foundation under Grant No. MCM20170206, The Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2018-k12, National Natural Science Foundation of China under Grant No. 61402210 and 60973137, Major National Project of High Resolution Earth Observation System under Grant No. 30-Y20A34-9010-15/17, State Grid Corporation Science and Technology Project under Grant No. SGGSKY00FJJS1700302, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA03030100, Google Research Awards and Google Faculty Award.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Electronic and Information EngineeringLanzhou Institute of TechnologyLanzhouChina
  3. 3.Department of Computer Science and Information EngineeringProvidence UniversityTaichungTaiwan

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