Search for: All records

With the rapid growth of technology, accessing digital health records has become increasingly easier. Especially mobile health technology like mHealth apps help users to manage their health information, as well as store, share and access medical records and treatment information. Along with this huge advancement, mHealth apps are increasingly at risk of exposing protected health information (PHI) when security measures are not adequately implemented. The Health Insurance Portability and Accountability Act (HIPAA) ensures the secure handling of PHI, and mHealth applications are required to comply with its standards. But it is unfortunate to note that many mobile and mHealth app developers, along with their security teams, lack sufficient awareness of HIPAA regulations, leading to inadequate implementation of compliance measures. Moreover, the implementation of HIPAA security should be integrated into applications from the earliest stages of development to ensure data security and regulatory adherence throughout the software lifecycle. This highlights the need for a comprehensive framework that supports developers from the initial stages of mHealth app development and fosters HIPAA compliance awareness among security teams and end users. An iOS framework has been designed for integration into the Integrated Development Environment(IDE), accompanied by a web application to visualize HIPAA security concerns in mHealth app development. The web application is intended to guide both developers and security teams on HIPAA compliance, offering insights on incorporating regulations into source code, with the IDE framework enabling the identification and resolution of compliance violations during development. The aim is to encourage the design of secure and compliant mHealth applications that effectively safeguard personal health information. 

This work introduces an novel approach to improving cybersecurity systems to focus on spam email-based cyberattacks. The proposed technique tackles the challenge of training Machine Learning (ML) models with limited data samples by leveraging Bidirectional Encoder Representations from Transformers (BERT) for contextualized embeddings. Unlike traditional embedding methods, BERT offers a nuanced representation of smaller datasets, enabling more effective ML model training. The methodology will use several pre-trained BERT models for generating contextualized embeddings using data samples, and these embeddings will be fed to various ML algorithms for effective training. This approach demonstrates that even with scarce data, BERT embeddings significantly enhance model performance compared to conventional embedding approaches like Word2Vec. The technique proves especially advantageous for insufficient instances of high-quality dataset. The result of this proposed work outperforms traditional techniques to mitigate phishing attacks with few data samples. This work provides a robust accuracy of 99.25% when we use multilingual BERT (M-BERT) to embed dataset.