International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Android Malware Detection using Machine Learning Techniques KNN-SVM, DBN and GRU

  • Sk Heena Kauser (Department of Computer Science & Engineering, Sathyabama Institute of Science and Technology) ;
  • V.Maria Anu (Department of Computer Science & Engineering, Sathyabama Institute of Science and Technology)
  • Received : 2023.07.05
  • Published : 2023.07.30
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Abstract

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

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References

  1. I. Martin, J. A. Hernandez, A. Munoz, and A. Guzman, "Android Malware Characterization Using Metadata and Machine Learning Techniques," Secur. Commun. Networks, 2018, doi: 10.1155/2018/5749481. 
  2. S. Priyadharshini and S. Shanthi, "A Survey on Detecting Android Malware Using Machine Learning Technique," 2021, doi: 10.1109/ICACCS51430.2021.9441712. 
  3. A. N. Jahromi, S. Hashemi, A. Dehghantanha, R. M. Parizi, and K. K. R. Choo, "An Enhanced Stacked LSTM Method with No Random Initialization for Malware Threat Hunting in Safety and Time-Critical Systems," IEEE Trans. Emerg. Top. Comput. Intell., 2020, doi: 10.1109/TETCI.2019.2910243. 
  4. J. Garcia, M. Hammad, and S. Malek, "Lightweight, obfuscation-Resilient detection and family identification of android malware," ACM Trans. Softw. Eng. Methodol., 2018, doi: 10.1145/3162625. 
  5. S. Gupta, S. Sethi, S. Chaudhary, and A. Arora, "Blockchain Based Detection of Android Malware using Ranked Permissions," Int. J. Eng. Adv. Technol., 2021, doi: 10.35940/ijeat.e2593.0610521. 
  6. M. Melis et al., "Do gradient-based explanations tell anything about adversarial robustness to android malware?," Int. J. Mach. Learn. Cybern., 2021, doi: 10.1007/s13042-021-01393-7. 
  7. S. Y. Yerima, S. Sezer, and I. Muttik, "High accuracy android malware detection using ensemble learning," IET Inf. Secur., 2015, doi: 10.1049/iet-ifs.2014.0099. 
  8. H. Zhang, D. Yao, and N. Ramakrishnan, "Causality-based sensemaking of network traffic for android application security," 2016, doi: 10.1145/2996758.2996760. 
  9. G. Canfora, F. Mercaldo, E. Medvet, and C. A. Visaggio, "Detecting Android malware using sequences of system calls," 2015, doi: 10.1145/2804345.2804349. 
  10. S. I. Imtiaz, S. ur Rehman, A. R. Javed, Z. Jalil, X. Liu, and W. S. Alnumay, "DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network," Futur. Gener. Comput. Syst., 2021, doi: 10.1016/j.future.2020.10.008. 
  11. J. Senanayake, H. Kalutarage, and M. O. Al-Kadri, "Android mobile malware detection using machine learning: A systematic review," Electronics (Switzerland). 2021, doi: 10.3390/electronics10131606. 
  12. E. J. Alqahtani, R. Zagrouba, and A. Almuhaideb, "A survey on android malware detection techniques using machine learning Algorithms," 2019, doi: 10.1109/SDS.2019.8768729. 
  13. O. C. Abikoye, B. A. Gyunka, and O. N. Akande, "Android malware detection through machine learning techniques: A review," Int. J. online Biomed. Eng., vol. 16, no. 2, pp. 14-30, 2020, doi: 10.3991/ijoe.v16i02.11549. 
  14. T. A. A. Abdullah, W. Ali, and R. Abdulghafor, "Empirical study on intelligent android malware detection based on supervised machine learning," Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 4, pp. 215-224, 2020, doi: 10.14569/IJACSA.2020.0110429. 
  15. F. Mercaldo and A. Santone, "Formal Equivalence Checking for Mobile Malware Detection and Family Classification," IEEE Trans. Softw. Eng., 2021, doi: 10.1109/TSE.2021.3067061. 
  16. S. M. Shahidi, H. Shakeri, and M. Jalali, "A semantic malware detection model based on the GMDH neural networks," Comput. Electr. Eng., 2021, doi: 10.1016/j.compeleceng.2021.107099.