Theses and Dissertations

ORCID

https://orcid.org/0009-0002-8531-0667

Advisor

Young, Maxwell

Committee Member

Mittal, Sudip

Committee Member

Banicescu, Ioana

Committee Member

Rahimi, Shahram

Date of Degree

5-10-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Computer Science

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Allocating resources in computer systems is a significant challenge due to constraints on resources, coordinating access to those resources, and tolerating malicious behavior. This dissertation investigates two fundamental problems concerning resource allocation. The first addresses the general challenge of sharing server resources among multiple clients, where an adversary may deny the availability of these resources; this is known as a denial-of-service (DoS) attack. Here, we propose a deterministic algorithm that employs resource burning (RB)—the verifiable expenditure of a network resource—to defend against DoS attacks. Specifically, our solution forces an adversary to incur higher RB costs compared to legitimate clients. Next, we develop a general policy-driven framework that utilizes machine learning classification to tune the amount of RB used for mitigating DoS attacks. Finally, we expand the application of RB to defend against DoS attacks on hash tables, which are a popular data structure in network applications. The second problem deals with resource allocation in wireless systems; specifically, the sharing of the wireless medium among multiple participants competing to transmit data. While modern WiFi and cellular standards do solve this problem, several recent theoretical results suggest that superior solutions are possible. Here, we investigate the viability of these solutions and discover that they fall short of their promised performance in practice. Consequently, we identify the cause of this shortcoming and quantify the discrepancy through a combination of analytical and simulation work. Ultimately, we propose a revised theoretical model that aligns better with practical observations.

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