In-Depth Analysis of Encryption Techniques for the Protection of Mobile Health Care Applications
Mobile healthcare applications, also known as mHealth apps, play an essential part in handling private patient information in today's healthcare system. In this article, the vital role that encryption plays in the process of data protection is investigated.
Wi-Fi Data Analysis based on Machine Learning
This study proposes using machine learning to improve Wi-Fi network security. As Wi-Fi networks spread from industrial to residential areas, the necessity for strong security has risen. The rise of smart networking, especially in the IoT, has created data security and vulnerability issues.
Enhancing Data Privacy of Medical Data through Encryption and Access Control
Electronic health records (EHRs) and the necessity for seamless information sharing across healthcare providers have made medical data management more complicated in the digital age. This study addresses the crucial topic of protecting medical data via encryption and access control.
Developing an Optimal Strategy to Address the Vulnerability of Image Tampering
Image tampering is a growing concern in numerous fields, necessitating robust solutions. This study investigates the creation of an optimal strategy to resolve the vulnerability of image tampering (manipulation)..
Including GRC Principles in IoT Security: A Comparison of Current Approaches and Future Prospects
With its ability to provide seamless communication between systems and objects, the Internet of Things (IoT) has completely changed the way we engage with technology.
Enhancing Organizational Time Efficiency Using Machine Learning for Employee Activity Monitoring
The present research work aims at identifying and proposing a machine learning based system that would effectively monitor the activities of the employees in an organization with a view to increasing the overall working time.
Federated Learning and GNNs for Explainable Network Intrusion Detection and Risk Prediction
The thesis focuses on a novel Network Intrusion Detection System (NIDS) based on Federated Learning (FL) and Graph Neural Networks (GNNs) for some of the most critical challenges in cybersecurity.
Automated Threat Intelligence, Detection and Response in Network Traffic Using Deep Learning Techniques (ATIDR)
The current study proposed the development of an automated TIDR system using deep learning to enhance detection and mitigation against network-based cyber threats