Risk assessment for sUAS in urban environments: a comprehensive analysis, modelling and validation for safe operations

The rapidly growing number of applications for small Unmanned Aerial Systems (sUAS) in last-mile applications within metropolitan environments creates a complex airspace and ground safety scenario, where numerous risks must be considered to accomplish safe operations. The built-up and heterogeneously shaped geometry, together with the densely populated and transited nature of urban scenes, define a challenging scene for piloted and autonomous missions, where operators need to consider performance-based and third-party risks. In response to the increasing requirements inherent from urban scenarios, this paper proposes an integrated and comprehensive risk model for urban sUAS operations, composed by different risk layers designed for real-world scenarios, and validated through simulated drone flights and 3D risk-based navigation. By identifying the different risk requirements for sUAS operations, first party risks – including navigation performance, data link monitoring and collision avoidance – are computed within a photorealistic simulation environment for a discretized airspace representation. On top of this, trajectory based third party risks are modelled to identify potential routes subject to drone failure and consequent fatality and third party damage, as well as societal impact in terms of noise and privacy. Risk-based navigation techniques are implemented to validate the resulting model, including classical path planning and reinforcement learning. The results enable the perception of urban scenes associated risks through the lenses of risk modelling, providing a valuable methodology for sUAS urban operations and contributing to safer drone flights

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to learn right MTD techniques that are effective against a rising number of heterogeneous zero-day attacks. Thus, this work presents CyberForce, a framework that combines Federated and Reinforcement Learning (FRL) to collaboratively and privately learn suitable MTD techniques for mitigating zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been deployed and evaluated in a scenario consisting of ten physical devices of a real IoT platform affected by heterogeneous malware samples. A pool of experiments has demonstrated that CyberForce learns the MTD technique mitigating each attack faster than existing RL-based centralized approaches. In addition, when various devices are exposed to different attacks, CyberForce benefits from knowledge transfer, leading to enhanced performance and reduced learning time in comparison to recent works. Finally, different aggregation algorithms used during the agent learning process provide CyberForce with notable robustness to malicious attacks.Comment: 11 pages, 8 figure

Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling

Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This article defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term

Detect and avoid considerations for safe sUAS operations in urban environments

Operations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. Higher demands in these operations and the requirement to access urban airspace present new challenges in sUAS operational safety. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements to its safe operation in urban environments according to the current legislation, such as the CAP 722 in the United Kingdom (UK). The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Different missions for the defined scenarios are designed and performed within the simulation model in Software Tool Kit (STK) software environment, covering a wide range of practical cases. The acquired data supports assessment of feasibility and requirements to real-time processing. Analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms

Security in Brain-Computer Interfaces: State-of-the-Art, Opportunities, and Future Challenges

Brain-Computer Interfaces (BCIs) have significantly improved the patients’ quality of life by restoring damaged hearing, sight, and movement capabilities. After evolving their application scenarios, the current trend of BCI is to enable new innovative brain-to-brain and brain-to-the-Internet communication paradigms. This technological advancement generates opportunities for attackers, since users’ personal information and physical integrity could be under tremendous risk. This work presents the existing versions of the BCI life-cycle and homogenizes them in a new approach that overcomes current limitations. After that, we offer a qualitative characterization of the security attacks affecting each phase of the BCI cycle to analyze their impacts and countermeasures documented in the literature. Finally, we reflect on lessons learned, highlighting research trends and future challenges concerning security on BCIs

Sense and avoid considerations for safe sUAS operations in urban environments

Operations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements for its safe operation in urban environments. The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Communication and navigation defined scenarios are designed and performed within the simulation model in Systems Tool Kit (STK) software environment, covering several practical cases. The acquired data supports the assessment of feasibility and requirements for real-time processing. Utilizing Unreal Engine and MATLAB analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms.Innovate UK funding, under the Grant number 75259. Thales iCase and EPSR

Study of P300 Detection Performance by Different P300 Speller Approaches Using Electroencephalography

Brain-Computer Interfaces (BCIs) are bidirectional devices that have allowed people to control computers or external devices through their brain activity. The P300 Speller is one of the most widely used BCI applications, where subjects can transmit textual information mentally with satisfactory performance. However, the P300 Speller still has room for improvement in practical use, such as selecting the best balance between accuracy and speed. Based on a lack of literature in this direction, this study evaluates two distinct approaches to the P300 Speller. The first is based on rows and columns following the traditional implementation, while the second is based on regions, employing subsets of characters during spelling. In both approaches, the effects of two different stimulus presentation parameters (the number of repetitions per stimulus and the interval between them) on the accuracy and performance efficiency of the P300 Speller are studied. The results show that both approaches obtain similar values in terms of detection performance, obtaining around 75% F1-score for predicting a character with four series of 12 blinks per character. In addition, the region-based approach presents a more robust scheme for false predictions, maintaining a similar spelling duration. The theoretical study performed indicates that spelling a character requires around one minute