International Journal of Science, Engineering and TechnologyAuthors: Anukeerthana D, Sathiya Shree S, Dr. P. Jeyanthi
Abstract: The exponential growth of Android applications has made the platform highly attractive to cybercriminals, who exploit its open-source nature and permission model to distribute malicious software. Traditional signature-based detection methods are ineffective against zero-day and obfuscated malware, while dynamic analysis approaches are computationally expensive and carry execution risks. This paper proposes the Intelligent Android Malware Detector, a web-based system that performs safe static analysis on Android Package Kit (APK) files. Key features, primarily requested permissions and application metadata, are extracted without executing the application. A Genetic Algorithm (GA) optimizes the feature set by selecting the most discriminative permissions and eliminating redundant ones, reducing dimensionality and improving model efficiency. The optimized features are then fed into an Artificial Neural Network (ANN) that learns complex patterns and outputs a malware probability score for nuanced risk assessment, achieving a classification accuracy of 92.26%. To enhance usability and awareness, the system incorporates a hybrid AI chatbot for explanatory support and a real-time threat intelligence module that aggregates cybersecurity news. Implemented using the Flask framework, the proposed solution offers a proactive, user-friendly, and scalable approach to Android malware detection, addressing key limitations of existing systems while promoting cybersecurity education.
DOI: https://doi.org/10.5281/zenodo.20415604
