Static, Dynamic and Intrinsic Features Based Android Malware Detection Using Machine Learning

  • Bilal Ahmad MantooEmail author
  • Surinder Singh Khurana
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)


Android is one of the smartest and advanced operating systems in the mobile phone market in the current era. The number of smartphone users based on the Android platform is rising swiftly which increases its popularity all over the world. The rising fame of this technology attracts everyone toward it and invites more number of hackers in Android platform. These hackers spread malicious application in the market and lead to the high chance of data leakage, financial loss and other damages. Therefore, malware detection techniques should be implemented to detect the malware smartly. Different techniques have been proposed using permission-based or system call-based approaches. In this paper, a hybrid approach of static, dynamic and intrinsic features based malware detection using k-nearest neighbors (k-NN) and logistic regression machine learning algorithms. The intrinsic feature contribution has also been evaluated. Furthermore, linear discriminant analysis technique has been implemented to evaluate the impact on the detection rate. The calculation uses a publicly available dataset of Androtrack. Based on the estimation results, both the k-nearest neighbors (k-NN) and logistic regression classifiers produced accuracy of 97.5%.


Dynamic analysis Static analysis Intrinsic features Logistic regression k-NN 


  1. 1.
    Handa, A., et al.: Malware detection using data mining techniques. Int. J. Adv. Res. Comput. Commun. Eng. 5, 2015 (2015)Google Scholar
  2. 2.
    Xialoeiwang, Y.Z.: Accurate malware detection in cloud. SpringerPlus, 123 (2015)Google Scholar
  3. 3.
    Hiranwal, et al.: A survey on techniques in detection and analysing malware executables. Int. J. Adv. Res. Comput. Commun. Eng. 3(4), 422–428 (2013)Google Scholar
  4. 4.
    Virus Total. Retrieved from virustotal.: (2015)
  5. 5.
    Install Android Studio. Retrieved from Developer Android: (2018)
  6. 6.
    Cortes, C.: Support-vector networks. Mach. Learn. 20, 273–297 (1995) (Kluwer Academic Publishers, Boston. Manufactured in The Netherlands)Google Scholar
  7. 7.
    Blog, K.Z.:. A complete guide to K-nearest-neighbors with applications in Python and R. Retrieved from kevinzakka.github: (2018)
  8. 8.
    How to use strace and itrace commands in Linux. Retrieved from the geek dairy: (2018)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science & TechnologyCentral University of PunjabBathindaIndia

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