Abstract The actuator is the critical component of the unmanned aerial vehicle (UAV). The interference and suppression to the signal of UAV’s actuators are challenging to directly detect or physically mitigate, thus posing a significant threat to UAV flight safety. Under the assumption of sensor integrity, the state-of-the-art physics-based attack detection approaches can identify the actuator attacks at runtime. However, when both sensor and actuator attacks are allowed simultaneously, such physics-based attack detection approaches cannot differentiate between the two physical attacks, thereby failing to locate the specific compromised actuators or maintain the UAV’s resilience to the actuator attack at runtime. This paper presents EADR , an efficient runtime framework for detecting and recovering from UAV actuator attacks. By leveraging the existing signal-characteristic-based sensor attack detection mechanism, EADR prevents potential sensor attacks from impacting the resilience of actuator attacks. In response to typical attack scenarios, we implement actuator attack detection based on the nonlinear dynamic model combined with the cumulative sum (CUSUM) detection algorithm. We further locate the specific compromised actuators, determine the required compensations for the signal of these actuators at runtime, and apply the compensations to the actuators to effectively recover the UAV system state. The experimental results demonstrate that the time to detection (TTD) of EADR ’s detector is significantly reduced compared with the state-of-the-art approaches. EADR ’s recovery mechanism can reduce the flight positional error by approximately 56.3 – 77.6%. The average runtime overhead of EADR is less than 2%, ensuring the real-time performance required for real-world UAV flight.

With the widespread adoption of UAV technology, the physical attacks targeting UAVs have become increasingly diverse, garnering growing attention. Physical attacks pose significant threats to the security of critical hardware within UAV systems, potentially leading to severe consequences such as crashes or unauthorized hijacking. Therefore, conducting in-depth research into physical attack methods on UAV systems not only provides theoretical support and strategic guidance for designing defense measures but also facilitates the optimization and tool-based application of existing attack techniques, paving new pathways for the development of anti-UAV technologies. This review begins with a systematic decomposition and detailed introduction of UAV systems from the perspective of hardware functional structures. Subsequently, it delves into vulnerabilities of UAV systems when facing physical attacks and provides a comprehensive review of existing physical attack methods. Particular attention is given to evaluating the effectiveness, technical characteristics, strengths, and limitations of these methods. Additionally, the review explores emerging physical attack techniques and the potential security threats posed by hardware extensions of UAVs in novel application domains. Furthermore, this review proposes a quantitative risk assessment framework for UAV security, systematically evaluating various physical attack methods based on attack cost, effectiveness, and likelihood. Finally, the review discusses future research directions in the domain of physical attacks on UAV systems, emphasizing the need to enhance existing technologies to strengthen anti-UAV capabilities and highlighting the importance of developing comprehensive defense strategies against physical attacks.

With the rapid development of the low-altitude economy, the application of UAVs has become increasingly widespread. However, the Global Navigation Satellite System (GNSS), which serves as a critical navigation technology for UAVs, is susceptible to spoofing attacks, severely impacting flight safety and mission accuracy. Many existing detection methods rely on additional equipment or multi-UAV cooperation for attack detection. This paper proposes an attitude angles-based GNSS spoofing detection method, AttDet, which leverages machine learning algorithm to model the change of UAV attitude and GNSS data. The method conducts feature analysis by examining the close dependency between GNSS data and attitude angle calculations, identifying attitude angles as key data for spoofing detection and extracting their statistical characteristics as feature data. Subsequently, flight experiments are designed to collect real-world data, and various machine learning algorithms are employed for training to select the optimal classifier, which is then deployed on the UAV. The system implements data acquisition and data pre-processing on the UAV, enabling online detection of GNSS spoofing attacks. Based on the collected real and spoofed data, the detection rate reaches 98.86%, with an equal error rate (EER) of 1.15%. Experimental evaluation and comparison demonstrate that this method outperforms existing detection approaches.

With the wide application of unmanned aerial vehicles (UAVs), security problems of UAV systems are gradually exposed, which bring great risks to UAV application. This article surveys the security of UAV systems, including attacks and countermeasures. First, the UAV system architecture is analyzed to identify security vulnerabilities. A UAV system contains the hardware platform, software platform, radio communication link, and application software. The navigation, guidance, and control systems are three core components in software platform. Then, security threats and attacks are analyzed and categorized from the view of cyberspace security, including spoofing attacks, reply attacks, jamming attacks, Denial-of-Service (DoS) attacks, eavesdropping attacks, side-channel attacks, manipulation attacks, and system intrusion attacks. Countermeasures are categorized based on the UAV system architecture and what attacks each countermeasure type can defend are also summarized. Compared to existing survey on security of a UAV system, more attack types and corresponding countermeasures are concluded. Finally, open issues and corresponding countermeasures on the security of UAV systems are discussed to guide the future research trend, including existing countermeasure weaknesses and challenges, and new issues and challenges.

The GPS is the most common used satellite-based navigation and positioning system. It is an indispensable component for a UAV as it provides accurate location data that is critical for navigation and mission success. However, GPS spoofing attacks are becoming a growing threat to GPS-dependent systems. Most existing GPS spoofing detection methods for UAVs are proposed based on simulation data or depend on multiple UAVs. In this paper, we propose a signal feature-based GPS spoofing detection approach, SigFeaDet, for a UAV using machine learning techniques. Our basic idea is to use the anomalies of GPS signal features due to the discrepancy between normal and spoofing signals. The Carrier-to-Noise Density Ratio (CN0) and Doppler frequency are significant signal features for GPS satellite positioning, which can be applied to identify spoofing signals. Various machine learning algorithms are applied to train with normal and spoofing data to choose the best classifier. The open GPS dataset TEXBAT is processed to obtain signal data and we also perform flight experiments to collect GPS data to augment normal GPS signal dataset. The detection rate is more than 94.8% and the equal error rate (EER) is about 5%, and the detection period is only 0.4 second.

Ensuring system safety is significant for safety-critical systems. To improve system safety in system architecture models, Architecture Analysis and Design Language (AADL) is used to model safety-critical systems. My thesis provides several safety analysis approaches for AADL models. To make it more effective, model transformation rules from AADL models to target formal models are formulated for the integration of formal methods into safety analysis approaches. The automatic transformation can reduce the degree of application difficulty of formal methods for engineers.

In this paper, we optimize power and spectrum allocation simultaneously to improve the demand-side communication quality in smart grid, to further reduce the cost of utility companies. The electricity cost is first modeled based on regulation errors caused by direct load control in the smart grid. Then the subbands are allocated to different data aggregator units according to the band confidence levels and the utility company's maximum cost. An algorithm is designed to optimize transmission power of the relay and refine the spectrum allocation to reduce the cost of utility companies. Simulation results demonstrate that the packet loss rate and cost of utility companies can be significantly reduced.

Quantitative verification is an effective technique for analyzing quantitative aspects of a safety critical system's design, and safety analysis is a significant aspect of safety critical system. However, they are often conducted separately. In this paper, we propose a new methodology, QaSten, fastens quantitative verification to safety analysis for Architecture Analysis and Design Language (AADL) model (including error model). QaSten formalizes a set of rigorous transformation rules that transform AADL model to PRISM model using formal method. In addition, QaSten can generate two safety property formulas automatically to check against the PRISM model for each hazardous state. Therefore, the occurrence probability of hazardous states can be calculated, which can help system designers understand the impact of parameters in the model. Furthermore, combining the probability and the severity of potential consequence of a hazardous state, QaSten determines the hazard risk acceptance level that can help engineers to identify critical hazard and modify or redesign architecture model to control it in an acceptable level. Two case studies, based on the Gas Leakage Alarm systems, are utilized to demonstrate QaSten's feasibility and effectiveness.

Safety analysis is a significant aspect of safety critical embedded systems. In this paper, an architecture-based hazard analysis method is presented to support safety assessment for Architecture Analysis and Design Language (AADL) model of embedded systems during early development phases. For further improving the hazard analytical ability of AADL, Hazard Model Annex is created. In order to improve the quality of system and the software development process, a safety model can be established by extending AADL model with error model and hazard model to specify fault behavior and hazard behavior of system. Hazard factor can be identified in safety model through hazard analysis. Additionally, conversion rules and formal methods are formulated to transform safety model into Deterministic Stochastic Petri Net (DSPN) for quantitative analysis using an existing tool. Finally, a safety analysis table is generated for overall evaluation of hazards, including hazard risk acceptance level, to help engineers to eliminate or control component hazards in an acceptance level. A small case study, based on fire alarm system, is utilized to demonstrate the feasibility of hazard analysis method for AADL model.

A 24 kB radiation hardened static random access memory using 180-nm commercial CMOS process appropriate for embedded system on a chip integrated circuits is presented. Radiation-hardened design is realized in the system, circuit and layout design to improve tolerance of radiation effects. The proto chips of SRAM are fabricated and tested, not only the electrical properties of SRAM chips are measured, but also the total ionizing dose effects experiments are finished using a Co-60 gamma radiation source. The experimental results show that, the TID(total ionizing dose effect) tolerance of SRAM chips is larger than 300 krad (Si) in which the electrical functions of SRAM are correct, but with the increase of TID rate, the static and dynamic current of SRAM increase seriously, and the write and read time increase slowly. Furthermore, it is verified by our research that, CMOS transistor layout design with ring-gate and P-type guard ring can enhance the TID tolerance of SRAM greatly.