Vein-Based Imaging Approach using Deep Learning for Real-Time Non-Invasive Dehydration Detection
Machine Learning, Image processing, Ubiquitous computing
Supervisor: Prof. Tanzima Hashem
We propose the design of a cost-free, easily accessible dehydration detection system that estimates a person's hydration status using a smartphone, without any hardware modifications or additional sensors. We create a novel dataset that includes hydration and dehydration images of each user including all the 4 landmarks. Besides, in order to evaluate biological factors, people from different ages and genders are considered. We present a novel process for selecting the best frame from hand-vein videos. Additionally, We propose two methods for extracting regions of interest (ROI): a light-weight, computational geometry-based method and a machine learning-based method. Now we are finalizing the paper to submit in IMWUT.

On Detecting Malicious Code Injection By Monitoring Multi-level Container Activites
Computer Security, Cloud Security
Supervisor: Prof. Md Shohrab Hossain
In recent years, cloud-native applications have been widely hosted and managed in containerized environments due to their unique benefits, such as being lightweight, portable, and cost-efficient. Their growing popularity makes them a common subject of cyberthreats, as evidenced by recent attacks. Many of those attacks take place due to malicious code injection to breach systems and steal sensitive data from a containerized environment. However, existing solutions fail to classify malicious code injection attacks that impact multiple levels (e.g., application and orchestrator). In this paper, we fill in this gap and propose a multi-level monitoring-based approach where we monitor container activities at both the system call level and the container orchestrator (e.g., Kubernetes) level. Specifically, our approach can distinguish between the expected and unexpected behavior of a container from various system call characteristics (e.g., sequence, frequency, etc.) along with the activities through event logs at the orchestrator level to detect malicious code injection attacks. We implement our approach for Kubernetes, a major container orchestrator, and evaluate it against various attack paths outlined by the Cloud Native Computing Foundation (CNCF), an open-source foundation for cloud native computing.