Computer Engineering Commons^™

An Optimized And Scalable Blockchain-Based Distributed Learning Platform For Consumer Iot, Zhaocheng Wang, Xueying Liu, Xinming Shao, Abdullah Alghamdi, Md. Shirajum Munir, Sujit Biswas

School of Cybersecurity Faculty Publications

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Data Analytics And Machine Learning To Enhance The Operational Visibility And Situation Awareness Of Smart Grid High Penetration Photovoltaic Systems, Aditya Sundararajan

FIU Electronic Theses and Dissertations

Electric utilities have limited operational visibility and situation awareness over grid-tied distributed photovoltaic systems (PV). This will pose a risk to grid stability when the PV penetration into a given feeder exceeds 60% of its peak or minimum daytime load. Third-party service providers offer only real-time monitoring but not accurate insights into system performance and prediction of productions. PV systems also increase the attack surface of distribution networks since they are not under the direct supervision and control of the utility security analysts.

Six key objectives were successfully achieved to enhance PV operational visibility and situation awareness: (1) conceptual cybersecurity …

A Hardware-Assisted Insider Threat Detection And Prevention Framework, Kyle W. Denney

FIU Electronic Theses and Dissertations

Today, the USB protocol is among the most widely used protocols. However, the mass-proliferation of USB has led to a threat vector wherein USB devices are assumed innocent, leaving computers open to an attack. Malicious USB devices can disguise themselves as benign devices to insert malicious commands to connected end devices. A rogue device appears benign to the average OS, requiring advanced detection schemes to identify malicious devices. However, using system-level hooks, advanced threats may subvert OS-reliant detection schemes. This thesis showcases USB-Watch, a hardware-based USB threat detection framework. The hardware can collect live USB traffic before the data can …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods

Undergraduate Research & Mentoring Program

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang

Research Collection School Of Computing and Information Systems

Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference …