Deteksi Malware menggunakan Metode Stacking berbasis Ensemble

Fauzi Adi Rafrastara; Catur Supriyanto; Cinantya Paramita; Yani Parti Astuti

doi:10.30591/jpit.v8i1.4606

Deteksi Malware menggunakan Metode Stacking berbasis Ensemble

Fauzi Adi Rafrastara, Catur Supriyanto, Cinantya Paramita, Yani Parti Astuti

Abstract

Serangan malware kian hari kian memprihatinkan. Evolusi malware yang cepat dan semakin destruktif menimbulkan kekhawatiran bagi banyak pihak. Oleh karena itu, deteksi malware yang efektif sangat dibutuhkan. Data mining memainkan peran yang krusial dalam bidang ini, mengingat algoritma-algoritma yang ada pada data mining bisa dilatih hingga menghasilkan akurasi yang paling tinggi. Untuk mengklasifikasi suatu file, apakah tergolong malware atau tidak, dalam penelitian ini metode stacking digunakan karena dapat meningkatkan akurasi jika dibandingkan dengan algoritma-algoritma klasifikasi konvensional. Empat Algoritma dilibatkan dalam eksperimen yang dilakukan, yaitu: Neural Network, Random Forest, kNN, dan Logistic Regression. Tiga algoritma pertama digunakan sebagai classifier pada level 0, sementara itu Logistic Regression digunakan classifier pada level 1 (meta classifier). Dengan kombinasi 4 algoritma tersebut, akurasi yang diperoleh adalah sebesar 98.7%, dan akurasi tersebut merupakan yang paling tinggi jika dibandingkan dengan masing-masing algoritma jika dieksekusi secara individual.

Keywords

deteksi malware, ensemble, stacking, data mining, neural network, random forest, kNN, logistic regression

Full Text:

References

O. Aslan and R. Samet, “A Comprehensive Review on Malware Detection Approaches,” IEEE Access, vol. 8, pp. 6249–6271, 2020, doi: 10.1109/ACCESS.2019.2963724.

F. A. Rafrastara and F. M. A, “Advanced Virus Monitoring and Analysis System,” 2011. [Online]. Available: http://sites.google.com/site/ijcsis/.

A. Pratama and F. A. Rafrastara, “Computer Worm Classification,” Int. J. Comput. Sci. Inf. Secur., vol. 10, no. 4, pp. 21–24, 2012.

M. Robles-Carrillo and P. García-Teodoro, “Ransomware: An Interdisciplinary Technical and Legal Approach,” Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/2806605.

W. Z. A. Zakaria, M. F. Abdollah, O. Mohd, M. S. M. M. Yassin, and A. Ariffin, “RENTAKA: A Novel Machine Learning Framework for Crypto-Ransomeware Pre-encryption Detection,” 2022. [Online]. Available: www.ijacsa.thesai.org.

P. Feng, J. Ma, C. Sun, X. Xu, and Y. Ma, “A novel dynamic android malware detection system with ensemble learning,” IEEE Access, vol. 6, pp. 30996–31011, 2018, doi: 10.1109/ACCESS.2018.2844349.

S. Shakya and M. Dave, “Analysis, Detection, and Classification of Android Malware using System Calls,” arXiv Prepr. arXiv2208.06130, 2022.

S. Aurangzeb, H. Anwar, M. A. Naeem, and M. Aleem, “BigRC-EML: big-data based ransomware classification using ensemble machine learning,” Cluster Comput., vol. 25, no. 5, pp. 3405–3422, 2022, doi: 10.1007/s10586-022-03569-4.

M. Rashid, J. Kamruzzaman, T. Imam, S. Wibowo, and S. Gordon, “A tree-based stacking ensemble technique with feature selection for network intrusion detection,” Appl. Intell., vol. 52, no. 9, pp. 9768–9781, 2022, doi: 10.1007/s10489-021-02968-1.

A. A. ABRO, E. TAŞCI, and A. UGUR, “A Stacking-based Ensemble Learning Method for Outlier Detection,” Balk. J. Electr. Comput. Eng., vol. 8, no. 2, pp. 181–185, 2020, doi: 10.17694/bajece.679662.

J. Yi, H. Zhang, H. Liu, G. Zhong, and G. Li, “Flight Delay Classification Prediction Based on Stacking Algorithm,” J. Adv. Transp., vol. 2021, 2021, doi: 10.1155/2021/4292778.

S. Touileb, “LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier,” Proc. Fifth Arab. Nat. Lang. Process. Work., pp. 313–319, 2020, [Online]. Available: https://aclanthology.org/2020.wanlp-1.34.

H. Byeon, “Exploring Factors Associated with the Social Discrimination Experience of Children from Multicultural Families in South Korea by using Stacking with Non-linear Algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 5, pp. 125–130, 2021, doi: 10.14569/IJACSA.2021.0120516.

Y. Wang, Q. Pan, X. Liu, and Y. Ding, “ET-MSF: a model stacking framework to identify electron transport proteins,” Front. Biosci. - Landmark, vol. 27, no. 1, pp. 1–9, 2022, doi: 10.31083/j.fbl2701012.

DOI: https://doi.org/10.30591/jpit.v8i1.4606