Android Malware Detection Using CTGAN-Based Data Augmentation and Autoencoder-Driven Feature Extraction

Enhanced Real Time Detection of Polymorphic Malware Using Synthetic Data Augmentation

The evolution of malware led to the emergence of polymorphic variants capable of continuously modifying their code structure to evade traditional detection mechanisms. Such adaptability renders conventional signature-based and heuristic approaches largely ineffective, creating a need for intelligent and adaptive security solutions. This research presents a framework for real-time detection of polymorphic malware, integrating deep learning and synthetic data augmentation to enhance robustness and generalization. The proposed system leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention LSTM along with other algorithms like Random Forest, Voting Classifier, Stacking Classifier and Naive Bayes to jointly analyze both static and dynamic features of malware, enabling the detection of complex behavioral patterns. To address the challenge of limited labeled datasets, a Generative Adversarial Network (GAN) is employed to generate synthetic data, thereby enriching the training samples and improving model diversity. The framework incorporates a data processing pipeline, including feature engineering, balancing, and real-time traffic analysis. To enhance transparency and trust, Explainable AI techniques such as LIME and SHAP are employed to interpret feature contributions and model decisions. Furthermore, a Flask-based web application is developed to enable real-time malware prediction through a user-friendly interface

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

Evaluation using metrics such as accuracy, precision, F1 score, and false positive rate indicates that CNN-GBM outperforms existing deep learning models, and enhancements stem from the effective integration of CNN feature extraction with GBM’s boosting capabilities.

Intelligent Android Malware Classification Using Equilibrium Optimizer and Deep LearningModel

An intelligent Android malware detection framework that combines deep learning with the Equilibrium Optimizer to improve detection performance is presented, providing an effective and reliable solution for securing Android devices against evolving malware threats.

Enhanced static analysis framework for multi-class Android malware detection using machine learning

Android Malware Detection Using an Optimized and Explainable Attentional Convolutional LSTM Model

Due to the increasing number of Android applications, its usage has increased which are exposing ourselves into complex mobile malware which is a serious threat on user privacy and system integrity. Machine Learning (ML) based detection methods are considered traditional and generally perform poorly in modeling the complex sequential activity of API-call patterns, typically having low interpretability of their decisions. To address these challenges, this work proposes an optimized and interpretable attentional CNN–LSTM framework for Android malware detection. It utilizes convolutional layer to extract spatial features, Long Short-Term Memory (LSTM) networks to capture patterns in sequential behavior and attention mechanism to highlight distinctive sequences of API calls. Moreover, Manta Ray Foraging Optimization (MWO) is applied for adaptive parameters fine-tuning and convergence improvement to enhances detection performance. We validate our approach with a wide range of experiments on two absolutely popular datasets for API call–based malware detection and result show high accuracy, precision, recall as well as F1-score owing to the 90:10 train–test split. Randomization-based statistical validation confirms the robustness and significance of results (p < 0.05, non-parametric tests). Besides classification performance, the proposed framework has an innate explainability as examining attention weights through API sequences allows for transparent interpretation of detection decisions. Malicious and benign applications exhibit different concentration patterns in the usage of APIs as revealed by attention heatmaps and top-weighted API analysis Hence, the proposed approach provides end to end, optimized and interpretable solution for reliable android malware detection.

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.