Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 12 references
Abstract
Malware severely threatens cybersecurity through Portable Executable (PE) files. In this study, the performances of machine learning and deep learning models for malware detection are compared using static analysis features extracted from PE files. Tree-based ensemble models like Random Forest, XGBoost, LightGBM, and CatBoost, along with MLP, DNN, and 1D-CNN deep learning architectures, were trained on a dataset comprising 62,485 samples and 15 features. Experimental results proved that ensemble algorithms outperform deep learning models on low-dimensional tabular data. Among the models, Random Forest achieved the highest success with 99.65% accuracy and a 99.59% F1-score. This academic study demonstrates that lightweight and high-performance tree-based models offer a more efficient solution for malware detection compared to computationally expensive deep neural networks.
Malware is a serious threat in the cybersecurity area because of its dynamic nature, the variety of malware families, stealth, propagation and the capability of evading traditional security products. Therefore, proper malware detection and classification are crucial for detecting malicious software and for securing computer systems from unauthorized access and data stealing, and for disrupting systems. This study covers all the bases when it comes to deep learning approaches for malware detection and classification. It covers the principles, different forms of malware, how to detect deep learning malware, how to represent data, obtaining features, and applications. The traditional detection methods are described with their drawbacks, namely based on signature, behavioral and heuristic methods. The report also delves into the methodologies used by deep learning to classify malware, namely CNNs and Bidirectional Long Short-Term Memory (BiLSTM) networks. BiLSTM models excel at learning sequential features from code-or behavior-related data, whereas CNN-based representation learning approaches excel at learning spatial features from malware representations. Moreover, the various detection techniques (static, dynamic and hybrid) are discussed so that their role in malware analysis can be understood. The survey identifies the current challenges and gaps in research and emphasizes the need for strong, scalable and adaptive deep-learning models to combat new malware threats and enhance cybersecurity protection.
Manjula Jain· International Journal of Cyb...· 0 citations
The results suggest that well-designed classical ensemble approaches are still quite competitive for scalable and interpretable static malware detection even with the rising usage of more and more powerful machine learning architectures.
A. Fauzan, Tito Pinandita, Aulia Desy Nur Utomo· International Journal of Adv...· 0 citations
Evaluated tree-based ensemble models, Random Forest, XGBoost, and LightGBM, for static malware detection in PE files show that tree-based ensembles outperform deep learning models, such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), as well as traditional machine learning approaches in handling high-dimensional tabular data.
Aleksandar Sandro Cvetković, S. Adamovic, Marko Šarac· SINTEZA· 0 citations
The findings indicate that combining deep representation learning with ensemble classification can improve dynamic malware detection and reduce dependence on static signatures.
Karthick Ganapathy· Journal of Computer and Fore...· 0 citations
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
Evaluation using metrics such as accuracy, precision, F1 score, and false positive rate indicates that CNN-GBM outperforms existing deep learning models, and enhancements stem from the effective integration of CNN feature extraction with GBM’s boosting capabilities.
C. Chimeleze, Norziana Jamil, Z. M. Zain et al.· Scientific Reports· 0 citations
