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

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.

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.

EXPLAINABLE MACHINE LEARNING-BASED MALWARE DETECTION USING PORTABLE EXECUTABLE STRUCTURAL FEATURES AND HYBRID VOTING ENSEMBLE

Objective: While evolving technologies have introduced advanced threat intelligence, traditional threats such as malware attacks continue to be a potential risk for contemporary computer systems and cybersecurity infrastructures and thus, it is of utmost importance that intelligent methods are developed in order to detect malware. Detecting malware variants is very difficult for traditional signature-based detection methods, especially for new and advanced malware. Thus, in this research work involves an explainable hybrid ensemble framework on machine learning approach with utilizing of Portable Executables (PE) file features for malware detection. Method: Our dataset is composed of 62,485 executable samples on the pre-processed and feature cleaning PE structures reduced to a domain matrix into 16 numerical features. We have implemented and evaluated multiple machine learning algorithms such as, Logistic Regression, Decision Tree, Random Forests, Support Vector Machine and Extreme Gradient Boosting. Voting Classifier was created for a robustness against classification and detection in addition to the hybrid ensemble model. Furthermore, we utilized SHAP analysis to understand model predictions and detect the most important features that contribute to malware classification. Results: Output from our practical experiments demonstrated some superior scores on all types of metrics measured. The Random Forest classifier has the best accuracy (99.64%) and the proposed Hybrid Voting model achieved an accuracy of 99.53%, precision: 99.68, recall: 99.24 and F1-score of 99.46 which confirms that our algorithm yielded very strong rumors and results are strongly stable across all compared datasets as well as high stability between individual classifiers (Fig From this analysis, we found that Dll Characteristics, Debug Size and Debug RVA stood out as the most significant features overall for our malware detection task, which showed how informative variations in executable structural characteristics can be to classification. Novelty: The findings demonstrate the potential of using ensemble learning in conjunction with explainable artificial intelligence techniques to enhance malware detection systems and cybersecurity applications.

Intelligent malware detection on Android smartphones via a hybrid approach using gradient boosting and convolutional neural network

Malware is malicious software that infiltrates systems without user consent. Effective detection involves identifying such software and distinguishing it from benign programs. While machine learning has shown promise in malware detection, many existing techniques demand extensive feature engineering and representation, leading to higher computation times and error rates. To address these limitations, we propose CNN-GBM, a hybrid model combining Convolutional neural networks (CNN) with Gradient Boosted Machines (GBM), aimed at improving Android malware detection. The approach begins by constructing a dataset comprising malware and benign files, represented as numerical feature vectors through n-grams, opcode sequences, or byte-level features. CNN processes the raw data to learn hierarchical patterns, while an entropy-based feature selection technique identifies the most informative attributes. These selected features are then fed into the GBM, trained to classify files as malware or benign. Evaluation using metrics such as accuracy, precision, F1 score, and false positive rate indicates that CNN-GBM outperforms existing deep learning models. Notable improvements include accuracy (+ 3%), precision (+ 1%), recall (+ 5%), AUC (+ 3.5%), and significant reductions in false positive rate (− 52%), false negative rate (− 15%), and error rate (− 35%). These enhancements stem from the effective integration of CNN feature extraction with GBM’s boosting capabilities.

A hybrid machine learning framework for dynamic malware classification using CNN, LSTM, and Random Forest models

Malware continues to evolve through polymorphic, metamorphic, and file-less techniques, reducing the effectiveness of signature-based detection. This study proposes a hybrid dynamic malware classification framework that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a Random Forest (RF) classifier. Dynamic behavioural traces are transformed into machine-readable features using Term Frequency-Inverse Document Frequency (TF-IDF), Count Vectorization, and Word2Vec embeddings. CNN and LSTM models are used as feature extractors to capture local and sequential behavioural patterns, while RF performs the final classification to improve robustness and interpretability. The reported experimental evaluation shows that the embedding-based hybrid CNN-RF configuration achieved the best performance, reaching 98.21% accuracy. The findings indicate that combining deep representation learning with ensemble classification can improve dynamic malware detection and reduce dependence on static signatures. The proposed framework is therefore a promising approach for adaptive malware classification and future security monitoring applications.