Art der Abschlussarbeit: Master (Bachelor)
Studiengang: Inf/ WInf/ CV/W&D Sci.
Kooperationsarbeit mit Fraunhofer FKIE
Introduction
The Internet of Things (IoT) is finding more and more applications in the public domain, and the number of open data sources keeps growing. This allows anyone to collect and analyse information from these sources. The information can be used for various purposes, such as traffic analysis, environmental monitoring, infrastructure protection, and even defence. But how can we verify that this data is trustworthy and has not been corrupted? This thesis investigates how to build trust in public IoT data and establish resilience against targeted misinformation by the information source and falsification by third parties, and what role artificial intelligence (AI) in particular can play in this regard.
Background
Using public IoT data for decision making and planning requires a high level of confidence in the integrity and accuracy of that data, especially in the context of a military operation. Incorrect or falsified information can lead to wrong decisions being made. Therefore, decision-relevant information is a high-value target for adversaries. Accordingly, it is important to develop mechanisms to build trust in public IoT data and to increase resilience against falsification. In this context, artificial intelligence offers promising potential to increase trust in public IoT data. By using AI-based algorithms, automated procedures can be developed to detect suspicious activity, identify anomalies, and verify the authenticity of IoT data. The use of AI can thus enable more effective detection and mitigation of tampering attempts to ensure the reliability and security of public IoT data in a military context.
Research Questions
What is the importance of trust in public IoT data for the acceptance and use of IoT applications in a civil/military context?
How can algorithms for detecting misinformation in public IoT data be developed and applied to ensure data integrity and trustworthiness? How can AI be used to enhance trust and detect misinformation?
What strategies and mechanisms can be used to verify the information source of public IoT data and effectively assess its trustworthiness?
What strategies and mechanisms can be used to increase the resilience of public IoT data against deliberate falsification, thereby increasing trust in data integrity?
What is the role of AI in these algorithms, policies, and mechanisms?
What best practices and recommendations, including AI, already exist to promote trust and improve the resilience of public IoT data in different civilian application domains (e.g., traffic analysis, environmental monitoring)?
How can identified (AI) mechanisms be implemented and evaluated in a laboratory demonstrator and what conclusions can be drawn?
Methodology
The work will focus on a comprehensive literature review first, looking at scientific papers and case studies to provide an overview of current methods for monitoring public IoT data for misinformation and falsification by third parties, and for increasing trust in this data. For this purpose, scientific literature as well as existing use cases will be reviewed and evaluated.
In a second step, a practical case study will be conducted, in which an AI-based solution for improving data integrity will be implemented and tested in a real IoT system. The focus will be on detecting anomalies in the collected data and investigating how well AI is able to detect and report such changes. For this purpose, a real IoT scenario will be designed and implemented in the lab using a demonstrator to investigate the benefits of AI with respect to the research questions.
Expected Results
As a result of this work, a comprehensive analysis of the existing challenges and vulnerabilities related to trust in public IoT data will be presented. For this purpose, a systematic review of existing literature and recent research results will be conducted, and the mechanisms described therein will be evaluated and compared. In addition, a laboratory demonstrator will be developed to test AI-based mechanisms for detecting anomalies in IoT data. The resulting findings will be analysed to derive a possible architecture for an AI-based detection system, but also to identify possible challenges and limitations in applying AI in IoT systems. Finally, the most important results of this thesis will be summarized, practical recommendations will be given, and future research directions will be pointed out.
Possible Paper Sources
· L. Wei, Y. Yang, J. Wu, C. Long and B. Li, "Trust Management for Internet of Things: A Comprehensive Study," in IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7664-7679, 15 May15, 2022, doi: 10.1109/JIOT.2021.3139989.
· A. Adewuyi, H. Cheng, Q. Shi, J. Cao, Á. MacDermott and X. Wang, "CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5432-5445, June 2019, doi: 10.1109/JIOT.2019.2902022.
· CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things | IEEE Journals & Magazine | IEEE Xplore Anomaly Detection in Internet of Things Using Artificial Intelligence: A Review" von R. Singh, P. Kumar, S. Tyagi, and S. Gupta
· A Trust Management Framework for the Internet of Things Using Artificial Intelligence" von S. Bhattacharya, A. Sengupta, and S. Roy
· Z. Lv, Y. Han, A. K. Singh, G. Manogaran and H. Lv, "Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence," in IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 1496-1504, Feb. 2021, doi: 10.1109/TII.2020.2994747.
