A Robust Deep Learning Framework for Intelligent Malware Detection

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.

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

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 Android Malware Classification Using Equilibrium Optimizer and Deep LearningModel

The rapid growth of Android applications has significantly increased the risk of malware attacks, making reliable malware detection an important cybersecurity challenge. Traditional detection methods often fail to identify newly emerging malware due to their dependence on predefined signatures and limited feature learning capabilities. This paper presents an intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance. Initially, Android application data are preprocessed to extract meaningful features for analysis. A Channel Attention Long ShortTerm Memory (CA-LSTM) model is employed to capture complex behavioral patterns and accurately distinguish malicious applications from benign ones. To further enhance the model's performance, the Equilibrium Optimizer is used to tune the hyperparameters, resulting in improved classification accuracy and faster convergence. Experimental evaluation on a benchmark Android malware dataset demonstrates that the proposed framework achieves superior detection performance compared with conventional machine learning methods, providing an effective and reliable solution for securing Android devices against evolving malware threats.

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

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.