Abstract

The cybersecurity of cyber-physical systems (CPS), particularly quadcopters, is critical due to their reliance on communication networks, which makes them vulnerable to cyber-attacks. This thesis addresses the security of quadcopters by introducing a novel framework for the simultaneous detection, identification, and isolation of cyber-attacks using Long Short-Term Memory (LSTM) networks. Unlike previous research that primarily focuses on detection, this work advances the field by integrating attack type identification and target isolation, enhancing overall security capabilities.
A contribution of this thesis is the emphasis on sequence generation as a pre-processing step for time-series data in LSTM models. By optimizing sequence length, overlap, and labeling methods, the proposed approach ensures the effective capture of temporal dependencies, substantially improving model performance for attack detection, identification, and isolation.
The study introduces a novel multi-output (MO) model for single quadcopters, utilizing a shared LSTM backbone with three output heads. This framework is extended to a network of quadcopters through a Multi-Input, Multi-Output (MIMO) architecture, which incorporates a flexible number of input heads for each quadcopter, enhancing scalability. The model supports both centralized and decentralized topologies, accommodating networks of varying sizes, ranging from 2 to 5 quadcopters.
Simulation results for Denial of Service (DoS), False Data Injection (FDI), and Replay attacks demonstrate the robustness of the proposed framework. The single quadcopter model achieved over 95% accuracy in attack detection, along with high precision in identifying attack types and locations. In networked setups, the centralized MIMO model delivered superior performance, while the decentralized approach also yielded promising results. These findings highlight the adaptability and effectiveness of the proposed approaches, paving the way for broader CPS applications and further advancements in sequence generation techniques.