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Human-out-of-the-Loop Swarm-based IoT Network Penetration Testing By IoT Devices

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.

Identiferoai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2762
Date15 August 2023
CreatorsSchiller, Thomas
PublisherSTARS
Source SetsUniversity of Central Florida
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceElectronic Theses and Dissertations, 2020-

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