Author Type

Graduate Student

Date of Award

2018

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Bou-Harb, Elias

Abstract

While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities generated by compromised IoT devices to facilitate prompt detection, mitigation and prevention of IoT exploitation. In this context, we initially provide a unique taxonomy, which sheds the light on IoT vulnerabilities from five di↵erent perspectives. Subsequently, we address the task of inference and characterization of IoT maliciousness by leveraging active and passive measurements. To support large-scale empirical data analytics in the context of IoT, we made available corresponding raw data through an authenticated platform.

Extent

137 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013089

Format

pdf

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