Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (2): 43-47.doi: 10.13190/j.jbupt.2016.02.009

• Papers • Previous Articles     Next Articles

Android Malware Detection Technology Based on Improved Naïve Bayesian

XU Yan-ping1, WU Chun-hua1, HOU Mei-jia2, ZHENG Kang-feng1, YAO Shan2   

  1. 1. Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. National Computer Network Emergency Response Technical Team/Coordination Center of China(CNCERT/CC), Beijing 100029, China
  • Received:2015-09-16 Online:2016-04-28 Published:2016-04-28

Abstract:

Permissions are extracted as features via static analysis. The information gain (IG) algorithm is applied to select significant features. The Naïve Bayesian (NB) classifier is created which is improved through Laplace calibration and natural logarithm of multiplier. The results with 10-fold cross validation indicate that the improved NB classifier achieves higher accuracy and precision, and the selected features by IG algorithm improve the detection efficiency in ensuring the accuracy of the case. Comparing k-nearest neighbor (KNN) and k-Means classifier, NB classifier has good performance on validity, accuracy and efficiency.

Key words: Android permission, malware application, information gain, Naï, ve Bayesian

CLC Number: