Thesis (M.S. & I.T.) -- Lincoln University, 2007. / "December 2007." Degree named as Master of Applied Computing on spine and use sheet. Also available via the World Wide Web.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3248. Adviser: Mark W. Spong. Includes bibliographical references (leaves 112-116) Available on microfilm from Pro Quest Information and Learning.

Drones are vulnerable to cyber-attacks due to their reliance on wireless networks for communication and control. This dissertation addresses this critical need by exploring novel methodologies for drone security through advanced anomaly detection systems and enhancing communication protocols. The research is organized around three main objectives: (1) detecting abnormalities in network-side operations of drones using machine learning (ML) algorithms, (2) developing control-side anomaly detection systems using recurrent neural networks (RNNs) and long short-term memory (LSTM) models, and (3) improving the security of the MAVLink protocol without altering its core structure. The study introduces DUDE-IDS, an intrusion detection system specifically designed for drone networks. The network-side IDS utilizes supervised ML algorithms such as Gradient Boosting, Linear SVC, Decision Tree, K-NN, and Random Forest, while the control-side IDS leverages LSTM model to detect deviations from normal operational patterns. A significant contribution of this research is the creation of labeled datasets specifically tailored for network-related and control-related cyber-attacks. These datasets are instrumental in developing and evaluating the effectiveness of the proposed detection mechanisms. The dissertation further demonstrates the practical application of DUDE-IDS in a real-time drone testbed which shows its suitability for resource-constrained environments. To address MAVLink protocol vulnerabilities, this research investigates advanced symmetric authenticated encryption (AEAD) techniques, such as ChaCha20-Poly1305, AES-GCM-SIV, AES-OCB3, and AES-CCM, into the protocol without modifying its lightweight structure. The performance of these encryption schemes is validated through real-time implementation on a custom-built drone platform that ensures a balance between security and computational efficiency. This dissertation makes important contributions to drone cybersecurity by providing robust detection mechanisms for both network-side and control-side anomalies and enhancing communication protocol security. The findings of this research lay the foundation for future work on lightweight IDS and secure communication protocols tailored to drone systems.

This thesis dissertation mainly compares and investigates laboratory study of different implementation methodologies of applied control systems and how they can be adopted in industrial, as well as commercial, automation applications. Namely the research paper aims to assess or evaluate eventual feedback control loops' performance and robustness over multiple conventional or state-of-the-art technologies in the field of applied industrial automation and instrumentation by implementing a laboratory case study setup: the ball on beam system. Hence, the paper tries to close the gap between industry and academia by: first, conducting a historical study and background information of main evolutional and technological eras in the field of industrial process control automation and instrumentation. Then, some related basic theoretical as well as practical concepts are reviewed in Chapter 2 of the report before displaying the detailed design. After that, the next Chapter, analyses the ball on beam control system problem as the case studied in the context of this research through reviewing previous literature, modeling and simulation. The following Chapter details the proposed design and implementation of the ball on beam case study as if it is under the introduced distributed industrial automation architecture. Finally, Chapter 5 concludes this work by listing several points leaned, remarks, and observations, and stating possible development and the future vision of this research.

Operating and interacting in an environment requires the ability to manage uncertainty and to choose definite courses of action. In this thesis we look to Bayesian probability theory as the means to achieve the former, and find that through rigorous application of the rules it prescribes we can, in theory, solve problems of decision making under uncertainty. Unfortunately such methodology is intractable in realworld problems, and thus approximation of one form or another is inevitable. Many techniques make use of heuristic procedures for managing uncertainty. We note that such methods suffer unreliable performance and rely on the specification of ad-hoc variables. Performance is often judged according to long-term asymptotic performance measures which we also believe ignores the most complex and relevant parts of the problem domain. We therefore look to develop principled approximate methods that preserve the meaning of Bayesian theory but operate with the scalability of heuristics. We start doing this by looking at function approximation in continuous state and action spaces using Gaussian Processes. We develop a novel family of covariance functions which allow tractable inference methods to accommodate some of the uncertainty lost by not following full Bayesian inference. We also investigate the exploration versus exploitation tradeoff in the context of the Multi-Armed Bandit, and demonstrate that principled approximations behave close to optimal behaviour and perform significantly better than heuristics on a range of experimental test beds.