Abstract

Networks of IoT devices are becoming increasingly important, but these networks are prone to cybersecurity issues. This work provides a novel approach for safer IoT networks: swarm-based IoT cybersecurity penetration testing by other IoT devices in the same network. To test this scenario, a simulation environment including three different penetration testing algorithms was developed. A linear penetration testing algorithm mimics human penetration testing activities and is used with a single agent and with multiple agents. A swarm-based algorithm utilizing queues adds communication between agents. The third algorithm is a swarm algorithm that uses Particle Swarm Optimization (PSO), thus adding a nature-based approach. All three algorithms are used to find vulnerabilities in simulated IoT networks of two different sizes. The networks are a smart home with 30 IoT devices and a smart building with 250 IoT devices. This study's results show the superiority of multi-agent approaches over linear, single-agent approaches to detecting unique vulnerabilities in a network. The swarm algorithms, which used communication between agents, outperformed the multi-agent approach with no communication. Additionally, the swarm algorithm utilizing queues demonstrated faster detection of vulnerabilities than the PSO algorithm. However, over time, the PSO outperformed the queue-based algorithm on the smart home scale. The smart building scale also provided faster detection for the queue-based algorithm than for the PSO. However, the PSO approach again provides better results over time and uses less computation time and memory resources.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Mondesire, Sean

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

School of Modeling, Simulation, and Training

Degree Program

Modeling & Simulation

Identifier

CFE0009791; DP0027899

URL

https://purls.library.ucf.edu/go/DP0027899

Language

English

Release Date

August 2024

Length of Campus-only Access

1 year

Access Status

Masters Thesis (Campus-only Access)

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