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Articles 1 - 5 of 5
Full-Text Articles in Systems Architecture
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Theses and Dissertations
Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …
Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao
Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao
Graduate Theses and Dissertations
Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.
Cloud computing has become more and more popular in …
Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff
Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff
All Graduate Theses, Dissertations, and Other Capstone Projects
Multiprocessor System-on-Chip (MPSoC) integrating heterogeneous processing elements (CPU, GPU, Accelerators, memory, I/O modules ,etc.) are the de-facto design choice to meet the ever-increasing performance/Watt requirements from modern computing machines. Although at consumer level the number of processing elements (PE) are limited to 8-16, for high end servers, the number of PEs can scale up to hundreds. A Network-on-Chip (NoC) is a microscale network that facilitates the packetized communication among the PEs in such complex computational systems. Due to the heterogeneous integration of the cores, execution of diverse (serial and parallel) applications on the PEs, application mapping strategies, and many other …
Carbon Footprint Of Machine Learning Algorithms, Gigi Hsueh
Carbon Footprint Of Machine Learning Algorithms, Gigi Hsueh
Senior Projects Spring 2020
With the rapid development of machine learning, deep learning has demonstrated superior performance over other types of learning. Research made possible by big data and high-end GPU's enabled those research advances at the expense of computation and environmental costs. This will not only slow down the advancement of deep learning research because not all researchers have access to such expensive hardware, but it also accelerates climate change with increasing carbon emissions. It is essential for machine learning research to obtain high levels of accuracy and efficiency without contributing to global warming. This paper discusses some of current approaches in estimating …
Countering Cybersecurity Vulnerabilities In The Power System, Fengli Zhang
Countering Cybersecurity Vulnerabilities In The Power System, Fengli Zhang
Graduate Theses and Dissertations
Security vulnerabilities in software pose an important threat to power grid security, which can be exploited by attackers if not properly addressed. Every month, many vulnerabilities are discovered and all the vulnerabilities must be remediated in a timely manner to reduce the chance of being exploited by attackers. In current practice, security operators have to manually analyze each vulnerability present in their assets and determine the remediation actions in a short time period, which involves a tremendous amount of human resources for electric utilities. To solve this problem, we propose a machine learning-based automation framework to automate vulnerability analysis and …