Unmanned aerial vehicles (UAVs) play a crucial role across diverse applications but remain vulnerable to sophisticated cyber-physical attacks targeting their sensor-control-actuation systems. Traditional recovery mechanisms often focus narrowly on sensor or control system disruptions, neglecting the interconnected vulnerabilities, particularly at the actuator level. To address these gaps, we propose SHIELD: Side-channel analysis-based multimodal Holistic Intrusion Evaluation with Layered Defense. It is a comprehensive security framework that leverages side-channel data for robust detection, precise attack categorization, and tailored recovery processes across the entire UAV system. Side channels provide critical, hard-to-manipulate information that enhances the detection of sophisticated, stealthy attacks. By categorizing the specific nature of an attack, SHIELD selects the most appropriate recovery strategy, focusing on the integrity of pulse width modulation (PWM) signals to ensure effective recovery and mission continuity. This makes SHIELD a holistic approach across the sensor-control-actuation spectrum.

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial noise in diverse HC environments. Our evaluations demonstrate SHEATH’s adaptability and effectiveness across diverse CNN configurations.

With the rise in the application of unmanned aerial vehicles (UAVs), security concerns associated with them have become paramount. Similar to other cyber-physical systems, the primary working principle behind UAVs follows the sensor-controller-actuation cycle. Errors between the setpoints and sensor data are computed through a PID controller and translated to pulse width modulated (PWM) signals that control the orientation and movement of a UAV. Recent research has demonstrated intentional electromagnetic interference (IEMI)-based alteration of PWM signals causing unauthorized maneuvers and crashes in UAVs. PWM alteration attacks can be carried out in various ways. For instance, hardware Trojans (HTs) can manipulate the PWM signals, and given the untrusted supply chain, HTs are a critical threat. Adversaries can exploit the PWM signals to manipulate UAV operations subtly, bypassing traditional intrusion detection systems (IDSs) that only monitor sensor data. Therefore, ensuring the integrity of PWM signals and their correlation with sensor and controller data is crucial for end-to-end UAV security. We address this need by proposing ConFIDe (Control-Fused Intrusion Detection system), a novel defense technique for UAVs. It verifies the integrity of the flight controller-generated PWM signals, ensuring the motors receive the signals free from hidden exploits. We validated our proposed IDS on different PWM alteration attack scenarios. In particular, we implemented a hardware Trojan attack targeting the PWM signals on a PX4-UAV to test the efficacy of the proposed IDS on a real system. ConFIDe performed well on all the attack scenarios, achieving a high ROC-AUC, including sensor attacks like GPS spoofing.

With the growing adoption of unmanned aerial vehicles (UAVs) across various domains, the security of their operations is paramount. UAVs, heavily dependent on GPS navigation, are at risk of jamming and spoofing cyberattacks, which can severely jeopardize their performance, safety, and mission integrity. Intrusion detection systems (IDSs) are typically employed as defense mechanisms, often leveraging traditional machine learning techniques. However, these IDSs are susceptible to adversarial attacks that exploit machine learning models by introducing input perturbations. In this work, we propose a novel IDS for UAVs to enhance resilience against such attacks using generative adversarial networks (GAN). We also comprehensively study several evasion-based adversarial attacks and utilize them to compare the performance of the proposed IDS with existing ones. The resilience is achieved by generating synthetic data based on the identified weak points in the IDS and incorporating these adversarial samples in the training process to regularize the learning. The evaluation results demonstrate that the proposed IDS is significantly robust against adversarial machine learning-based attacks compared to the state-of-the-art IDSs while maintaining a low false positive rate.

The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory for a swarm of UAVs depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacities, and budget limitations. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follows the requirements while the tool scales well with the problem size.