Jul 2026· IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies· pp. 310-315· 0 citations· 21 references
Abstract
Modern malwares utilize different obfuscation techniques to hide their behaviors and overcome traditional signature-based detection methods. This paper investigates the use of machine learning techniques to detect obfuscated malware in the Windows operating system trained on memory-based features. This research considers different malware types and analyzes several obfuscation techniques used by malwares to bypass antivirus detection. An ensemble machine learning model is developed based on four algorithms: SVM, Random Forest, Gradient Boosting, and CatBoost. Voting is used for classification based on results of each model. The ensemble model is evaluated using a dataset of obfuscated malware samples, namely CIC-MalMem-2022, and validated through cross-validation. Experimental results show high detection rate where the accuracy and F1-score are 99.99% and 99%, respectively. The results show that machine learning can enhance malware detection against obfuscated malware threats.
Experimental results demonstrate that the proposed approach achieves high classification performance while improving transparency in malware detection decisions, making it suitable for practical cybersecurity applications.
V. Padmapriya, S. Uma, S. Sumathi et al.· International journal of com...· 0 citations
The swift growth in the quantity of Android applications has resulted in a similar increase in the threat of malware, compromising the privacy and security of users. This research introduces a framework aimed at efficiently identifying malware within Android applications through machine learning techniques. Various cla...
S. Ramya, Baratam Sahith, Bokka Teenanjani et al.· 2026 4th International Confe...· 0 citations
A malware detection method based on Federated Machine Learning using a publicly available dataset of Portable Executable and Object Linking and Embedding files from the Windows environment and the Gradient-weighted Class Activation Mapping++ algorithm to highlight the image regions that influenced the classification re...
Giovanni Ciaramella, Fabio Martinelli, Antonella Santone et al.· International Conference on...· 0 citations
The results demonstrate that GNN-based malware detection not only addresses the limitations of conventional approaches in terms of scalability but also provides a more robust and adaptable framework that could be integrated into future real-time threat intelligence and automated defense systems.
Wurood A. Jbara, N. A. Hussein· Al-Noor Journal of Engineeri...· 0 citations
Malware has become one of the biggest threats to computer systems and digital networks, affecting individuals, businesses, and government organizations. Traditional malware detection methods mainly depend on signatures and predefined rules, making them less effective against newly developed and constantly evolving atta...
S.Srikar, G.Rajini· International Journal of Dat...· 0 citations
XEnDroid (Xplainable Ensemble Droid), an advanced stacking ensemble approach for detecting malware on Android phones by combining random forest, convolution neural network, and transformer models under a logistic regression model is proposed.
M. B., V. R, Surjith K. et al.· Journal of ISMAC· 0 citations
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