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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.
Date15 August 2023
CreatorsSchiller, Thomas
Source SetsUniversity of Central Florida
Detected LanguageEnglish
SourceElectronic Theses and Dissertations, 2020-

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