Enhancing Mobile Security Through Biometric Authentication and Variation Autoencoders for Zero-Day Attack Detection

Mobile devices like smartphones, tablets, and wearables have become essential tools for daily activities, enabling seamless shopping, socializing, and financial transactions. These devices, coupled with faster internet access, have transformed human interaction and commerce, offering remote banking services and real-time connections across distances. However, this widespread adoption has also led to a rise in mobile malware attacks, particularly zero-day attacks that exploit previously unknown vulnerabilities. Current detection methods, which rely on known malware signatures, are ineffective against such threats, highlighting the need for advanced systems that can adapt to emerging risks. This paper proposes a system that uses Variational Autoencoders (VAEs) to detect anomalous behaviors in mobile devices indicative of zero-day malware. VAEs model the normal activity patterns on mobile devices, allowing for the detection of deviations that may suggest malicious activity. The study also explores integrating biometric authentication, such as fingerprint and facial recognition, to enhance anomaly detection and protect user privacy. This approach aims to prevent unauthorized access and reduce the risk of data breaches. The research ultimately seeks to contribute to the development of more secure mobile platforms, offering protection against emerging threats like identity theft and ransomware.