International Journal of Mathematics And Computer Research

Abstract

Smartphones have become indispensable in daily life, serving as hubs for communication, financial transactions, and data storage. However, their ubiquity and functionality make them prime targets for cyberattacks. This study delves into the development and enhancement of hacking detection mechanisms tailored to smartphones. It reviews existing techniques, including behavioural analytics, anomaly detection, machine learning, and intrusion detection systems, while identifying gaps in their effectiveness. A comprehensive framework for real-time detection is proposed, integrating multi-layered security measures, data encryption, and AI-driven analysis. Through simulations and case studies, the framework demonstrates improved detection rates and reduced false positives compared to traditional methods. This paper emphasizes the critical need for proactive, adaptive solutions to counter emerging threats in the rapidly evolving landscape of mobile security.

The study explores the benefits of biometric identification over more conventional techniques like smart cards, PINs, and passwords since biometric qualities are hard to copy or steal. The study also looks at the advantages and disadvantages of the various kinds of biometric authentication systems. The promise of biometric authentication as a practical and safe means of identification in a range of applications is emphasized in the paper's conclusion.