Intelligent Android Malware Classification Using Equilibrium Optimizer and Deep LearningModel

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.

Optimizing Android Malware Detection with Joint Feature Selection and Hidden Node Tuning: A BIGSA-HNO-ELM Framework

The growing complexity and scale of Android malware present significant challenges for the development of detection systems that are both accurate and computationally efficient. Traditional machine learning methods often struggle with high-dimensional feature spaces and poorly optimized model architectures, leading to degraded accuracy and excessive resource consumption. To address these limitations, we propose a unified malware detection framework—BIGSA-HNO-ELM—that integrates Binary Improved Gravitational Search Algorithm (BIGSA) for robust feature selection with Hidden Node Optimization (HNO) applied to an Extreme Learning Machine (ELM) classifier. Evaluated on the KronoDroid dataset comprising over 78,000 Android applications with 464 hybrid static and dynamic behavioural features, the proposed framework achieves a 77% reduction in feature dimensionality, significantly lowering computational overhead while preserving high detection accuracy. Through HNO, the optimal number of ELM hidden neurons is dynamically determined, enhancing generalization and reducing overfitting. Comprehensive experiments demonstrate that the BIGSA-HNO-ELM model achieves 98.0% accuracy, 97.4 % precision, 96.2 % recall, 96.8 % F1-Score and an AUC-ROC of 97.0%, substantially outperforming baseline methods. Statistical significance testing (paired t-tests and Wilcoxon signed-rank tests, p < 0.01) confirms the robustness of these performance gains and demonstrates superior stability across 10-fold cross-validation with minimal variance in performance metrics. This integrated optimization approach not only improves predictive performance but also supports deployment in resource-constrained and real-time environments, making BIGSA-HNO-ELM a compelling solution for modern Android malware detection.

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

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.

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.

AI-Based Adaptive Malware Detection Using Portable Executable (PE) Header Analysis and Hybrid Machine Learning

Experimental results demonstrate that the proposed approach achieves high classification performance while improving transparency in malware detection decisions, making it suitable for practical cybersecurity applications.