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
With the increasing threat of malware across various software related domains, malware detection and classification is critical to determine the response actions. Different strategies have been adopted to address the challenge of malware detection. With the advent of deep learning techniques, malware detection using image processing has garnered research attention. In this work, we proposed a novel malware binary to image transformation technique HilEnT based on a combination of Hilbert curve-based transformation of malware binary and the entropy feature comparison of malware file with benign and malware classes. Three grayscale images produced during this process are combined to form a three-channel colored image which is then used for malware detection using machine learning techniques. We performed supervised binary and multiclass classification to evaluate the effectiveness of our proposed HilEnT. We also evaluated a few-shot learning technique to assess the robustness of our proposed HilEnT in a practical setting where the number of available class samples is limited. Furthermore, we investigated the benefits of combination of Histogram of Oriented Gradients and Principal Component Analysis for time performance improvements through feature reduction techniques. We evaluated our proposed methodology on four datasets: Dike, Michael Lester Dataset, Microsoft BIG 2015 and a self-collected dataset, and achieved the state-of-the-art results.
Rahul Kale, Thesath Wijayasiri, K. Fok et al.· 0 citations
A Hybrid Neural Network–Convolutional Neural Network (NN–CNN) Deep Learning Framework for malware detection, malware-family classification, and malware-variant identification and considers two important issues in practical malware detection: model explainability and generalization to previously unseen malware.
Chioma Grace Nwankwo, B. C. Amanze, Ikechukwu Amaefule· World Journal of Advanced Re...· 0 citations
Malicious Software (Malware) is an abusive term defined as malicious pieces of code or program scripts that can damage information technology systems. In 2025, malware remained one of the most critical cybersecurity threats, with over 6.5 billion attacks globally. There are almost 560,000 new malware samples daily and more than 75 percent of organizations experiencing ransomware attacks annually. AI driven attacks, cloud exploitation and mobile malware has significantly increased both the scale and sophistication of cyber threats. Classification of malware is very important in terms of ensuring the security of information systems. In literature, many studies have been done to classify malware so far. This study presents a convolutional neural network (CNN) architecture based on transfer learning using EfficientNetB4 for multi-class image classification. The model leverages pretrained weights from ImageNet and integrates a custom classification head. The proposed approach improves classification accuracy while reducing training time and overfitting compared to conventional CNN models trained from scratch.
Abdul Hanan, M. Rizwan, Sana Tariq et al.· ICACNC 2026 Proceedings· 0 citations
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.
Alessandro Stamerra, Vincenzo Paduano, Andrea Iannacone· 2026 6th International Confe...· 0 citations
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.
Gokay Mutlu, Neşe Rihani, E. Bayazit· Signal Processing and Commun...· 0 citations