A Hybrid Model for Android Malware Detection using Decision Tree and KNN

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Illustrates the diverse types of malware classification. A few of the common malware are viruses, botnets, spyware as well as ransomware
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Published: Dec 15, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i1s.5899
Keywords:
Android Malware, Decision Tree, KNN, Precision, Recall, Smartphone

Main Article Content

Heena Kauser.Sk
Research scholar, Department of Computer Science & Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
Maria Anu.V
Professor, Department of Computer Science & Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

Article Details

How to Cite
Kauser.Sk, H. ., & Anu.V, M. . (2022). A Hybrid Model for Android Malware Detection using Decision Tree and KNN. International Journal on Recent and Innovation Trends in Computing and Communication, 10(1s), 321–328. https://doi.org/10.17762/ijritcc.v10i1s.5899
Issue
Vol. 10 No. 1s (2022): Special Issue on Enhancement and Innovations in Exploring Engineering
Section
Articles

References

H. Zhou, X. Yang, H. Pan, and W. Guo, “An Android Malware Detection Approach Based on SIMGRU,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3007571.

O. C. Abikoye, B. A. Gyunka, and O. N. Akande, “Android malware detection through machine learning techniques: A review,” Int. J. online Biomed. Eng., 2020, doi: 10.3991/ijoe.v16i02.11549.

P. Feng, J. Ma, C. Sun, X. Xu, and Y. Ma, “A novel dynamic android malware detection system with ensemble learning,” IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2844349.

Y. C. Chen, H. Y. Chen, T. Takahashi, B. Sun, and T. N. Lin, “Impact of Code Deobfuscation and Feature Interaction in Android Malware Detection,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3110408.

A. T. Kabakus, “What static analysis can utmost offer for android malware detection,” Inf. Technol. Control, 2019, doi: 10.5755/j01.itc.48.2.21457.

Z. Ren, H. Wu, Q. Ning, I. Hussain, and B. Chen, “End-to-end malware detection for android IoT devices using deep learning,” Ad Hoc Networks, 2020, doi: 10.1016/j.adhoc.2020.102098.

S. Y. Yerima and S. Sezer, “DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection,” IEEE Trans. Cybern., 2019, doi: 10.1109/TCYB.2017.2777960Y.

J. Xu, Y. Li, R. H. Deng, and K. Xu, “SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering,” IEEE Trans. Dependable Secur. Comput., 2022, doi: 10.1109/TDSC.2020.3005088.

T. Kim, B. Kang, M. Rho, S. Sezer, and E. G. Im, “A multimodal deep learning method for android malware detection using various features,” IEEE Trans. Inf. Forensics Secur., 2019, doi: 10.1109/TIFS.2018.2866319.

X. Xiao, S. Zhang, F. Mercaldo, G. Hu, and A. K. Sangaiah, “Android malware detection based on system call sequences and LSTM,” Multimed. Tools Appl., 2019, doi: 10.1007/s11042-017-5104-0.

X. Liu, X. Du, X. Zhang, Q. Zhu, H. Wang, and M. Guizani, “Adversarial samples on android malware detection systems for IoT systems,” Sensors (Switzerland), 2019, doi: 10.3390/s19040974.

J. Lee, H. Jang, S. Ha, and Y. Yoon, “Android malware detection using machine learning with feature selection based on the genetic algorithm,” Mathematics, 2021, doi: 10.3390/math9212813.

Y. Yang, X. Du, Z. Yang, and X. Liu, “Android malware detection based on structural features of the function call graph,” Electron., 2021, doi: 10.3390/electronics10020186.

X. Chen et al., “Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection,” IEEE Trans. Inf. Forensics Secur., 2020, doi: 10.1109/TIFS.2019.2932228.

X. Jiang, B. Mao, J. Guan, and X. Huang, “Android Malware Detection Using Fine-Grained Features,” Sci. Program., 2020, doi: 10.1155/2020/5190138.

P. Palumbo, L. Sayfullina, D. Komashinskiy, E. Eirola, and J. Karhunen, “A pragmatic android malware detection procedure,” Comput. Secur., 2017, doi: 10.1016/j.cose.2017.07.013.

S. Y. Yerima, S. Sezer, and I. Muttik, “Android malware detection using parallel machine learning classifiers,” 2014. doi: 10.1109/NGMAST.2014.23.

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.

J. D. Koli, “RanDroid: Android malware detection using random machine learning classifiers,” 2018. doi: 10.1109/ICSESP.2018.8376705.

R. Agrawal, V. Shah, S. Chavan, G. Gourshete, and N. Shaikh, “Android Malware Detection Using Machine Learning,” 2020. doi: 10.1109/ic-ETITE47903.2020.491.

S. Y. Yerima, M. K. Alzaylaee, A. Shajan, and P. Vinod, “Deep learning techniques for android botnet detection,” Electron., 2021, doi: 10.3390/electronics10040519.

“Graph Approach for android malware detection using machine learning techniques,” Humanit. Nat. Sci. J., 2021, doi: 10.53796/hnsj21115.

M. Kedziora, P. Gawin, M. Szczepanik, and I. Jozwiak, “ANDROID MALWARE DETECTION USING MACHINE LEARNING AND REVERSE ENGINEERING,” 2018. doi: 10.5121/csit.2018.81709.

R. Taheri, R. Javidan, M. Shojafar, Z. Pooranian, A. Miri, and M. Conti, “On defending against label flipping attacks on malware detection systems,” Neural Comput. Appl., 2020, doi: 10.1007/s00521-020-04831-9.

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., 2020, doi: 10.14569/IJACSA.2020.0110429.

B. A. Gyunka and S. I. Barda, “Anomaly detection of android malware using One-Class K-Nearest Neighbours (OC-KNN),” Niger. J. Technol., 2020, doi: 10.4314/njt.v39i2.25.

Y. Pan, X. Ge, C. Fang, and Y. Fan, “A Systematic Literature Review of Android Malware Detection Using Static Analysis,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3002842.

C. Li, K. Mills, D. Niu, R. Zhu, H. Zhang, and H. Kinawi, “Android Malware Detection Based on Factorization Machine,” IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2958927.

K. Liu, S. Xu, G. Xu, M. Zhang, D. Sun, and H. Liu, “A Review of Android Malware Detection Approaches Based on Machine Learning,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3006143.

S. K. Sasidharan and C. Thomas, “ProDroid — An Android malware detection framework based on profile hidden Markov model,” Pervasive Mob. Comput., 2021, doi: 10.1016/j.pmcj.2021.101336.

H. Chen, Z. Li, Q. Jiang, A. Rasool, and L. Chen, “A hierarchical approach for android malware detection using authorization-sensitive features,” Electron., 2021, doi: 10.3390/electronics10040432.

W. Zhang, N. Luktarhan, C. Ding, and B. Lu, “Android Malware detection using tcn with bytecode image,” Symmetry (Basel)., 2021, doi: 10.3390/sym13071107.

H. Sun, G. Xu, Z. Wu, and R. Quan, “Android Malware Detection Based on Feature Selection and Weight Measurement,” Intell. Autom. Soft Comput., vol. 33, pp. 585–600, Jan. 2022, doi: 10.32604/iasc.2022.023874.