A Survey on Deep Learning Approaches for Malware Detection and Classification

DEVELOPMENT OF A HYBRID NN–CNN DEEP LEARNING FRAMEWORK FOR INTELLIGENT MALWARE DETECTION, FAMILY CLASSIFICATION, AND VARIANT IDENTIFICATION

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.

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

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.

Malware Classification using Transfer Learning and EfficientNetB4 on Malevis-Datasets

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.

A Review of Explainable Machine Learning Methods for Malware Detection and Classification

Traditional cybersecurity solutions have been greatly challenged by the fast growth of malware, making accurate and interpretable malware detection crucial. In recent years, deep learning (DL) and machine learning (ML) have gained traction as potent methods for identifying malware, both known and undiscovered, polymorphic, and zero-day. These methods learn intricate patterns from both static and dynamic data analysis. Many ML and DL models, however, are opaque and untrustworthy because to their black-box design, which is particularly problematic for applications that rely on security. Malware detection and categorisation using explainable machine learning approaches is thoroughly reviewed in this study. Starting with a general introduction to malware detection and the most frequent kinds of malware, it moves on to cover the three main classical detection approaches: signature-based, behavioral-based, and heuristic-based. Advanced malware detection approaches based on ML and DL are further examined in the paper, which highlights frequently used algorithms, their working principles, and benefits. Along with that, it delves into XAI approaches like LIME, KernelSHAP, and Shapley values, which are model-agnostic, to enhance the interpretability of malware detection models. These techniques use transparent machine learning models and both global and local explanations. Accumulated Local Effects (ALE), Individual Conditional Expectation (ICE), and Partial Dependence Plot (PDP) are among the visual methods of explanation that are covered. The study concludes with a review of the literature, an analysis of the current state of affairs, and a plan for the future of research into the topic of malware detection systems as it pertains to building confidence among users and facilitating educated cybersecurity decisions.