A Multi-Modal Deep Learning Framework for Robust Polymorphic Malware Detection

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

Hybrid noise-resistant technique for malware classification.

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 and represents one of the first comprehensive investigations of hybrid latent-texture representation learning within a memory-forensics malware visualization setting.

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.

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.

Hybrid Convolutional-Hopfield Neural Networks for Image-Based Malware Classification

A novel hybrid CNN-Hopfield Neural Network (CNN-HNN) framework that replaces traditional dense classification heads with continuous Modern Hopfield associative memory layers is proposed, achieving top-tier accuracy and reducing classification-head parameter counts while reducing floating-point operations (FLOPs) and accelerating inference latency to under 2 ms per sample.

Hybrid Feature Image Mapping (HFIM): A Multi-Channel Image Approach for Malware Detection Using Static and Dynamic Features

It is indicated that integrating static and dynamic features at the representation level can improve robustness and classification performance in image-based malware detection, particularly under variations in feature availability.