Komparasi Kinerja CPU dan Memori dalam Proses Klasifikasi Malware Menggunakan Algoritma Random Forest pada Sistem Operasi Kali Linux 64-bit dan Ubuntu 64-bit

Achmad Luthfan Aufar Hindami; Dimas Rifqi Firmansyah; Christopher Ralin Anggoman; Aqwam Rosadi Kardian

doi:10.24114/cess.v9i1.53994

Authors

Achmad Luthfan Aufar Hindami Politeknik Siber dan Sandi Negara
Dimas Rifqi Firmansyah Politeknik Siber dan Sandi Negara
Christopher Ralin Anggoman Politeknik Siber dan Sandi Negara
Aqwam Rosadi Kardian STMIK Jakarta STI&K

DOI:

https://doi.org/10.24114/cess.v9i1.53994

Keywords:

kali linux, machine learning, malware, random forest, sistem operasi, ubuntu

Abstract

Machine learning telah menjadi aspek krusial dalam keamanan siber, khususnya dalam deteksi intrusi dan klasifikasi malware. Namun, penerapan teknik ini memerlukan alokasi sumber daya komputasi yang signifikan. Dalam konteks ini, sistem operasi memiliki peran krusial berkaitan dengan kemampuannya dalam mengelola sumber daya komputasi. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa CPU dan memori dari dua sistem operasi populer, yaitu Kali Linux dan Ubuntu, dalam konteks komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning untuk mengetahui sistem operasi dengan performa yang lebih baik. Keduanya diuji menggunakan model machine learning dan variasi dataset yang sama untuk klasifikasi malware menggunakan algoritma Random Forest. Analisis dilakukan dengan membandingkan persentase konsumsi CPU dan memori antar kedua sistem operasi. Berdasarkan hasil pengujian, ditemukan bahwa sistem operasi Kali Linux memiliki rata-rata penggunaan CPU yang lebih rendah sekitar 19,64%, dan penggunaan memori yang lebih rendah sekitar 0,06% dibandingkan dengan sistem operasi Ubuntu. Dengan demikian, dapat disimpulkan bahwa sistem operasi Kali Linux memiliki performa yang lebih baik daripada sistem operasi Ubuntu dalam hal konsumsi CPU dan memori dalam komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning.

References

M. I. Jordan and T. M. Mitchell, œMachine learning: Trends, perspectives, and prospects, Science (1979), vol. 349, no. 6245, pp. 253“255, Jul. 2015, doi: 10.1126/science.aaa8415.

M. Molina, œMachine Learning for Sociology 2019.

W. Nabi, A. Bansal, and B. Xu, œApplications of artificial intelligence and machine learning approaches in echocardiography, Echocardiography, vol. 38, no. 6. Blackwell Publishing Inc., pp. 982“992, Jun. 01, 2021. doi: 10.1111/echo.15048.

T. Kabbani and F. E. Usta, œPredicting the Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark, Jan. 2022, [Online]. Available: http://arxiv.org/abs/2201.12283

Z. Zhang, H. Al Hamadi, E. Damiani, C. Y. Yeun, and F. Taher, œExplainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research.

N. Milosevic, A. Dehghantanha, and K. K. R. Choo, œMachine learning aided Android malware classification, Computers and Electrical Engineering, vol. 61, pp. 266“274, Jul. 2017, doi: 10.1016/j.compeleceng.2017.02.013.

M. K. Alzaylaee, S. Y. Yerima, and S. Sezer, œDL-Droid: Deep learning based android malware detection using real devices, Comput Secur, vol. 89, Feb. 2020, doi: 10.1016/j.cose.2019.101663.

M. S. Malik, œMachine Learning in Malware Detection, LGU International Journal for Electronic Crime Investigation, vol. 5, no. 3, pp. 29“36, 2021.

F. A. Kraemer, D. Palma, A. E. Braten, and D. Ammar, œOperationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management, IEEE Internet Things J, vol. 7, no. 12, pp. 11803“11814, Dec. 2020, doi: 10.1109/JIOT.2020.3002330.

S. J. Lee et al., œIMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction, IEEE Access, vol. 8, pp. 65520“65529, 2020, doi: 10.1109/ACCESS.2020.2985089.

P. Nalmpantis, R. Kalamatianos, K. Kordas, and K. Kermanidis, œLow resources prepositional phrase attachment, in Proceedings - 14th Panhellenic Conference on Informatics, PCI 2010, 2010, pp. 78“82. doi: 10.1109/PCI.2010.34.

K. L. Lai Khine and T. T. Soe Nyunt, œPredictive analytics on high-dimensional big data using principal component regression (PCR), in Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, WCSE 2019 SPRING, International Workshop on Computer Science and Engineering (WCSE), 2019, pp. 148“153. doi: 10.18178/wcse.2019.03.025.

X. Zhou, J. Zhang, J. Wan, L. Zhou, Z. Wei, and J. Zhang, œScheduling-Efficient Framework for Neural Network on Heterogeneous Distributed Systems and Mobile Edge Computing Systems, IEEE Access, vol. 7, pp. 171853“171863, 2019, doi: 10.1109/ACCESS.2019.2954897.

S. Aridhi and E. Mephu Nguifo, œBig Graph Mining: Frameworks and Techniques, Big Data Research, vol. 6. Elsevier Inc., pp. 1“10, Dec. 01, 2016. doi: 10.1016/j.bdr.2016.07.002.

J. Whitehill, G. Littlewort, I. Fasel, M. Bartlett, and J. Movellan, œToward practical smile detection, IEEE Trans Pattern Anal Mach Intell, vol. 31, no. 11, pp. 2106“2111, 2009, doi: 10.1109/TPAMI.2009.42.

A. Shawahna, S. M. Sait, and A. El-Maleh, œFPGA-Based accelerators of deep learning networks for learning and classification: A review, IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers Inc., pp. 7823“7859, 2019. doi: 10.1109/ACCESS.2018.2890150.

C. Y. Mansa Baidoo, W. Yaokumah, and E. Owusu, œEstimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection, International Journal of Information Technologies and Systems Approach, vol. 16, no. 1, Feb. 2023, doi: 10.4018/IJITSA.316889.

V. R. Dasari, M. S. B. Im, and L. Beshaj, œSolving machine learning optimization problems using quantum computers, 2020. [Online]. Available: https://www.researchgate.net/publication/336197360

T. S. Gunawan, M. K. Lim, N. F. Zulkurnain, and M. Kartiwi, œOn the review and setup of security audit using Kali Linux, Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 1. Institute of Advanced Engineering and Science, pp. 51“59, Jul. 01, 2018. doi: 10.11591/ijeecs.v11.i1.pp51-59.

J. Park and Y. I. Eom, œFile Fragmentation from the Perspective of I/O Control The 14th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage).

B. D. Merrill, A. T. Ward, J. H. Grose, and S. Hope, œSoftware-based analysis of bacteriophage genomes, physical ends, and packaging strategies, BMC Genomics, vol. 17, no. 1, Aug. 2016, doi: 10.1186/s12864-016-3018-2.

S. Narayan, P. Shang, and N. Fan, œPerformance evaluation of IPv4 and IPv6 on Windows Vista and Linux Ubuntu, in Proceedings - International Conference on Networks Security, Wireless Communications and Trusted Computing, NSWCTC 2009, 2009, pp. 653“656. doi: 10.1109/NSWCTC.2009.368.

A. W. Daher, A. Rizik, M. Muselli, H. Chible, and D. D. Caviglia, œPorting rulex software to the raspberry pi for machine learning applications on the edge, Sensors, vol. 21, no. 19, Oct. 2021, doi: 10.3390/s21196526.

F. Kanavati et al., œWeakly-supervised learning for lung carcinoma classification using deep learning, Sci Rep, vol. 10, no. 1, Dec. 2020, doi: 10.1038/s41598-020-66333-x.

Hemavati, V. S. Devi, and R. Aparna, œMulti-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation, International Journal of Electrical and Computer Engineering, vol. 13, no. 3, pp. 3384“3397, Jun. 2023, doi: 10.11591/ijece.v13i3.pp3384-3397.

D. R. Cutler et al., œRANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY, 2007.

X. Fu, Y. Chen, J. Yan, Y. Chen, and F. Xu, œBGRF: A broad granular random forest algorithm, Journal of Intelligent and Fuzzy Systems, vol. 44, no. 5, pp. 8103“8117, May 2023, doi: 10.3233/JIFS-223960.

E. V. A. Sylvester et al., œApplications of random forest feature selection for fine-scale genetic population assignment, Evol Appl, vol. 11, no. 2, pp. 153“165, Feb. 2018, doi: 10.1111/eva.12524.

Y. Chen and R. Zhang, œResearch on Credit Card Default Prediction Based on k- Means SMOTE and BP Neural Network, Complexity, vol. 2021, 2021, doi: 10.1155/2021/6618841.

D. Ghatrehsamani, C. Denninnart, J. Bacik, and M. Amini Salehi, œThe Art of CPU-Pinning: Evaluating and Improving the Performance of Virtualization and Containerization Platforms, in ACM International Conference Proceeding Series, Association for Computing Machinery, Aug. 2020. doi: 10.1145/3404397.3404442.

S. B. Mishra, œHANDBOOK OF RESEARCH METHODOLOGY, 2017. [Online]. Available: https://www.researchgate.net/publication/319207471