Revolutionizing Mobile Sensor Data Authentication with Finance AI and Advanced Deep Learning Techniques

EasyChair Preprint 14629

Abstract

In an era where mobile devices are ubiquitous, the integrity and authenticity of the data generated by mobile sensors have become critical concerns. The increasing reliance on mobile sensors for various applications, from healthcare monitoring to autonomous vehicles, necessitates robust data authentication methods. This article explores the application of deep machine learning models to enhance the authentication of mobile sensor data. We delve into the challenges posed by conventional authentication techniques and demonstrate how deep learning models can overcome these limitations. By analyzing a comprehensive dataset of mobile sensor outputs, we highlight the superior performance of deep learning models in detecting anomalies and ensuring data integrity. The results indicate a significant improvement in accuracy and reliability compared to traditional methods. This advancement not only secures the data transmitted by mobile sensors but also opens avenues for future research in enhancing mobile security protocols using advanced machine learning techniques.

Keyphrases: Data Integrity, Deep Learning Models, Explainable AI (XAI), Hybrid Authentication Systems, IoT Security, LSTM networks, Mobile Sensor Data Authentication, Transfer Learning, adversarial attacks, real-time processing

@booklet{EasyChair:14629,
  author    = {Alakitan Samad},
  title     = {Revolutionizing Mobile Sensor Data Authentication with Finance AI and Advanced Deep Learning Techniques},
  howpublished = {EasyChair Preprint 14629},
  year      = {EasyChair, 2024}}