HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

RES2NET-MAXOUT: Image-Based Multi-Class Malware Classification with Deep Learning Models

A Survey on Deep Learning Approaches for Malware Detection and Classification

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.

MITHRIL: Malware Classification Through Lightweight cnn and Uniform Sampling

As the cybersecurity landscape continues to evolve, the relentless spread of sophisticated malware poses a substantial threat to the integrity and security of digital systems. A strategic approach to software analysis is malware visualization, a constantly evolving technique that seeks to transform the complex binary code of malware into visual representations by directly mapping the bits of binary code to pixels. The result is then used as training material for computer vision approaches using machine learning or artificial intelligence. Although this approach is being explored with increasingly complex machine learning/artificial intelligence models, it has not been explored in the ever-growing market of smart and other devices, which are typically limited in terms of hardware and computing power. Therefore, we aim to present a reliable and robust approach to malware analysis through visualization, suitable for machines with limited resources and capable of being installed on every node in the network. The proposed approach was tested on two datasets, namely MalImg, Malevis and a variant of the latter, namely Malevis without the 'Other' class, on which three different preprocessing approaches for resource-constrained machines were applied: a linear scaling approach, a bicubic scaling approach, and a strategic image resampling approach on the same hardware. The results show that the proposed pre-processing methods, combined with relatively simple convolutional neural network architectures, achieve the best weighted F1 Score of 96.75% with inference time of 0.2570 seconds compared to more complex models, which achieved the best metric performance of 99.40% and inference time of 1.5982 seconds, representing an overall time cost approximately 6.5 times higher. The proposed resampling approach was also shown to be faster at image conversion and outperform the commonly used image resizing approach.

Hybrid noise-resistant technique for malware classification.

Visualization-based malware detection has recently gained significant attention for binary and multiclass malware classification using machine learning and deep learning techniques. However, existing visualization-based frameworks still face several important limitations, including insufficient robustness evaluation, limited cross-dataset validation, restricted malware diversity, and difficulty distinguishing visually similar and noise-sensitive malware families. In many cases, the visual similarity between malware classes and the presence of perturbations negatively affect feature extraction quality, leading to degraded classification performance and reduced generalization capability. To address these challenges, this study proposes a novel hybrid malware representation framework that integrates Convolutional Autoencoder (CAE)-based latent structural learning with Local Binary Pattern (LBP)-based texture feature extraction for robust malware classification. To the best of our knowledge, this study represents one of the first comprehensive investigations of hybrid latent-texture representation learning within a memory-forensics malware visualization setting while jointly addressing robustness, perturbation resilience, scalability, and cross-dataset generalization through a unified evaluation framework. The proposed framework combines global hierarchical representations learned through CAE with fine-grained local texture descriptors extracted using LBP to improve the discrimination of visually similar malware families and enhance robustness against noisy visualization conditions. The extracted features are subsequently evaluated using multiple machine learning classifiers, where XGBoost achieved the highest performance with an accuracy of 99.90%, precision of 99.79%, recall of 99.92%, and F1-score of 99.85%. To comprehensively evaluate the proposed framework, extensive experiments are conducted using both a memory-forensics malware dataset and the large-scale BODMAS dataset containing 134,435 PE malware samples spanning 581 malware families. The experimental evaluation incorporates cross-validation, ablation analysis, robustness assessment under multiple perturbation conditions, and feature-space visualization analysis. The results demonstrate that the proposed CAE+LBP framework consistently outperforms standalone feature extraction approaches and conventional end-to-end CNN models while maintaining strong robustness and cross-dataset generalization capability across diverse malware distributions and noisy conditions.

A Robust Deep Learning Framework for Intelligent Malware Detection

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 attacks. As cybercriminals continue to use advanced techniques such as code obfuscation and polymorphism, there is a growing need for smarter and faster detection methods. This project proposes a robust malware detection system using deep learning to identify both known and unknown malware with improved accuracy. The model automatically learns meaningful patterns from malware data without relying heavily on manual feature extraction. By analyzing the behavior and characteristics of malicious files, the proposed system can classify malware efficiently while reducing false alarms. The approach improves detection speed, enhances security, and supports real-time threat analysis. This makes it a reliable solution for protecting modern computer systems against rapidly changing cyber threats.

A Comparative Analysis of Machine Learning and Deep Learning Methods for Malware Detection in Portable Executable Files

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.