Modern Deep Learning Approaches for Malware Detection and Classification

TMalware continues to evolve in complexity, employing evasion tactics that challenge traditional detection methods.In response, deep learning has emerged as a powerful approach to automate and enhance malware detection across static and dynamic analysis domains.This paper provides a comprehensive review of recent advances in deep learning-based detection systems, with particular emphasis on hybrid models that integrate static code features and runtime behavioral indicators.We examine key architectural approaches including convolutional neural networks for spatial pattern recognition, recurrent neural networks for sequential data analysis, graph neural networks for structural understanding and transformers for contextaware, multi-modal inference.Benchmarks such as EMBER, Drebin and Malicia are discussed as standard datasets supporting reproducibility and comparative evaluation.Case studies of prominent malware families such as WannaCry, TrickBot and Emotet illustrate the operational relevance of hybrid approaches.In addition, we explore emerging trends such as federated learning for privacy-preserving collaboration, multimodal architectures for enriched feature learning, lightweight models for edge-based detection and adversarial defences for model robustness.Persistent challenges include limited labelled data, the interpretability of model decisions and the need to address concept drift in evolving threats.This review highlights the growing maturity of deep learning techniques in cybersecurity and outlines future directions for building more resilient, efficient and explainable malware detection frameworks.