Specification and Verification of an IoT terrorist attack system using ThingML/Coloured Petri Nets
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Date
2024
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Abstract
This thesis presents a comprehensive methodology for the specification, verification,
and implementation of a security system using a combination of Unified Modeling Language
(UML), ThingML, CPN Tools, and Petri Nets. The approach ensures a robust
framework for designing secure systems and validating their functionality before deployment.
Initially, the security system’s requirements and architecture are modeled using UML,
providing a clear and structured representation of system components and interactions.
ThingML is then utilized to specify the detailed behavior of the system, allowing for seamless
integration with Internet of Things (IoT) devices and ensuring platform-independent
design.
To ensure the correctness and reliability of the specified system, Colored Petri Nets
(CPN) are employed for formal verification. Using CPN Tools, the system’s models
are analyzed for potential errors, deadlocks, and inefficiencies, ensuring a high level of
confidence in the system’s performance and security.
As a case study, the thesis details the development of a web application using Django,
designed for intruder detection through facial recognition. This application is integrated
with IoT devices, enabling real-time monitoring and response to security breaches. The
facial recognition system accurately identifies intruders, and the integration with IoT
allows for immediate alerts and automated actions.