Detection and Analysis of Malicious Software Using Machine Learning Models
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 (M...
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| Format: | Article |
| Language: | English |
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Sakarya University
2024-08-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3952776 |
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| author | Selman Hızal Ahmet Öztürk |
| author_facet | Selman Hızal Ahmet Öztürk |
| author_sort | Selman Hızal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1f8d2177d0a4fd7ab837be988a4100e |
| institution | DOAJ |
| issn | 2636-8129 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-e1f8d2177d0a4fd7ab837be988a4100e2025-08-20T03:10:47ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292024-08-017226427610.35377/saucis...148923728Detection and Analysis of Malicious Software Using Machine Learning ModelsSelman Hızal0https://orcid.org/0000-0001-6345-0066Ahmet Öztürk1https://orcid.org/0009-0009-5228-7596SAKARYA UNIVERSITY OF APPLIED SCIENCESSAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİThe 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.https://dergipark.org.tr/en/download/article-file/3952776information securitysoftware analysismalware detection systemmachine learning |
| spellingShingle | Selman Hızal Ahmet Öztürk Detection and Analysis of Malicious Software Using Machine Learning Models Sakarya University Journal of Computer and Information Sciences information security software analysis malware detection system machine learning |
| title | Detection and Analysis of Malicious Software Using Machine Learning Models |
| title_full | Detection and Analysis of Malicious Software Using Machine Learning Models |
| title_fullStr | Detection and Analysis of Malicious Software Using Machine Learning Models |
| title_full_unstemmed | Detection and Analysis of Malicious Software Using Machine Learning Models |
| title_short | Detection and Analysis of Malicious Software Using Machine Learning Models |
| title_sort | detection and analysis of malicious software using machine learning models |
| topic | information security software analysis malware detection system machine learning |
| url | https://dergipark.org.tr/en/download/article-file/3952776 |
| work_keys_str_mv | AT selmanhızal detectionandanalysisofmalicioussoftwareusingmachinelearningmodels AT ahmetozturk detectionandanalysisofmalicioussoftwareusingmachinelearningmodels |