User Privacy Protection via Windows Registry Hooking and Runtime Encryption.

The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository.

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning.

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification.

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach.

Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs.

An automatic medication self-management and monitoring system for independently living patients.

This paper describes the development, prototyping, and evaluation of RMAIS (RFID-based Medication Adherence Intelligence System). Previous work in this field has resulted in devices that are either costly or too complicated for general (especially elderly) patients to operate. RMAIS provides a practical and economical means for ordinary patients to easily manage their own medications, taking the right dosage of medicine at the prescribed time in a fully automatic way.

RMAIS: RFID-based medication Adherence Intelligence System.

There has been compelling evidence that outpatients, especially those who are elderly or taking multiple complexly scheduled drugs, are not taking their medicines as directed, leading to unnecessary disease progression, complications, functional disabilities, lower quality of life, and even mortality. Existing technologies for monitoring and improving drug adherence are either costly or too complicated for general patients to use. In this paper, we introduce the detailed design and the complete prototype of a marketable Radio-Frequency Identification (RFID)-based Medication Adherence Intelligence System (RMAIS) that can be conveniently used at a residential home by ordinary patients.