Detection and Analysis of Malicious Software Using Machine Learning Models

Ahmet Öztürk; Selman Hızal

doi:10.35377/saucis...1489237

EN

Detection and Analysis of Malicious Software Using Machine Learning Models

Abstract

The continuous evolution of malware poses a significant challenge in cybersecurity, adapting to technological advancements despite implemented security measures. This paper introduces an innovative approach to enhance the detection of obfuscated malware through the integration of machine learning (ML). Utilizing a real-world dataset of prevalent malware types such as spyware, ransomware, and trojan horses, our study addresses the evolving challenges of cybersecurity. In this study, we evaluate the performance of ML algorithms for obfuscated malware detection using the CIC-MalMem-2022 dataset. Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. Experimental results demonstrate the effectiveness of RF, J-48, and XGBoost in achieving high accuracy rates across different classification tasks. NB also shows competitive performance but faces challenges in handling imbalanced datasets and multi-class classification. Our findings highlight the importance of employing advanced ML techniques for enhancing obfuscated malware detection capabilities and provide valuable insights for cybersecurity practitioners and researchers. Future research directions include fine-tuning model hyperparameters, exploring ensemble learning approaches, and expanding evaluation to diverse datasets and real-world scenarios.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software , Software Engineering (Other)

Journal Section

Research Article

Authors

Ahmet Öztürk ^*
0009-0009-5228-7596
Türkiye

Selman Hızal
0000-0001-6345-0066
Türkiye

Early Pub Date

August 26, 2024

Publication Date

August 31, 2024

Submission Date

May 24, 2024

Acceptance Date

August 19, 2024

Published in Issue

Year 2024 Volume: 7 Number: 2

DOI

https://doi.org/10.35377/saucis...1489237

IZ

https://izlik.org/JA28UR37CC

Cite

RIS / Bibtex

APA

Öztürk, A., & Hızal, S. (2024). Detection and Analysis of Malicious Software Using Machine Learning Models. Sakarya University Journal of Computer and Information Sciences, 7(2), 264-276. https://doi.org/10.35377/saucis...1489237

AMA

1.Öztürk A, Hızal S. Detection and Analysis of Malicious Software Using Machine Learning Models. SAUCIS. 2024;7(2):264-276. doi:10.35377/saucis.1489237

Chicago

Öztürk, Ahmet, and Selman Hızal. 2024. “Detection and Analysis of Malicious Software Using Machine Learning Models”. Sakarya University Journal of Computer and Information Sciences 7 (2): 264-76. https://doi.org/10.35377/saucis. 1489237.

EndNote

Öztürk A, Hızal S (August 1, 2024) Detection and Analysis of Malicious Software Using Machine Learning Models. Sakarya University Journal of Computer and Information Sciences 7 2 264–276.

IEEE

[1]A. Öztürk and S. Hızal, “Detection and Analysis of Malicious Software Using Machine Learning Models”, SAUCIS, vol. 7, no. 2, pp. 264–276, Aug. 2024, doi: 10.35377/saucis...1489237.

ISNAD

Öztürk, Ahmet - Hızal, Selman. “Detection and Analysis of Malicious Software Using Machine Learning Models”. Sakarya University Journal of Computer and Information Sciences 7/2 (August 1, 2024): 264-276. https://doi.org/10.35377/saucis. 1489237.

JAMA

1.Öztürk A, Hızal S. Detection and Analysis of Malicious Software Using Machine Learning Models. SAUCIS. 2024;7:264–276.

MLA

Öztürk, Ahmet, and Selman Hızal. “Detection and Analysis of Malicious Software Using Machine Learning Models”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, Aug. 2024, pp. 264-76, doi:10.35377/saucis. 1489237.

Vancouver

1.Ahmet Öztürk, Selman Hızal. Detection and Analysis of Malicious Software Using Machine Learning Models. SAUCIS. 2024 Aug. 1;7(2):264-76. doi:10.35377/saucis. 1489237