Current Developments in Autonomous and Remotely Piloted Aerial Systems (UAS)
The field of autonomous and remotely piloted aerial systems (UAS) is experiencing significant advancements, particularly in the areas of intent modeling, cyber security, and secure control systems. Recent developments are pushing the boundaries of what these systems can achieve, focusing on enhancing both their operational capabilities and security measures.
Intent Modeling and Inference
One of the key directions in the field is the development of sophisticated intent modeling and inference frameworks. These frameworks are designed to assist in defense planning against unauthorized flights, particularly within geo-fenced areas. The introduction of novel mathematical definitions for intent, along with the concepts of critical waypoints and patterns, allows for a more comprehensive characterization of UAS missions. This approach is not only applicable to autonomous systems but also extends to semi-autonomous and piloted systems operating in complex 2D and 3D environments with obstacles. The use of deep learning methodologies, such as attention-based bi-directional long short-term memory (Bi-LSTM) networks, for intent inference is a notable innovation, enabling more accurate and efficient detection and tracking of potential threats.
Cyber Security in Operational Technology
Another significant area of progress is the application of advanced reinforcement learning techniques to improve data efficiency and overall performance in operational technology cyber security. The integration of action masking and curriculum learning has shown promising results in enhancing the realism of cyber-attack simulations, leading to more effective training of defensive agents. These techniques have demonstrated the ability to reach high levels of performance in significantly fewer timesteps compared to traditional methods, making them highly data-efficient. The introduction of a hardcoded defensive agent based on cyber security best practices provides a valuable benchmark for evaluating the performance of reinforcement learning agents.
Secure Offloading in MEC Systems
The intersection of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) is also seeing innovative developments, particularly in the area of secure offloading. The combination of non-orthogonal multiple access (NOMA) technology with UAV-based MEC systems presents unique challenges, such as the vulnerability to eavesdropping by malicious entities. Recent work has focused on developing secure offloading schemes that minimize network computational costs while ensuring the security of offloaded data. The use of deep deterministic policy gradient algorithms to solve complex optimization problems in high-dimensional action spaces is a notable advancement in this area.
Resilient Location Sharing Services
Ensuring the safety and reliability of UAVs in dynamic environments is another critical focus. The development of resilient location sharing services, such as FlySafe, addresses the need for up-to-date and accurate location information to prevent collisions and maintain spatial awareness. These services leverage opportunistic delivery mechanisms and prioritize the freshness of location data, making them resilient to false location injection threats. The high accuracy and reliability demonstrated in simulations highlight the potential of these services to enhance the safety and operational efficiency of UAVs.
Security Against Stealthy Attacks
Finally, the field is also advancing in the area of security against stealthy attacks on UAVs. Recent studies have analyzed the vulnerabilities of UAVs to black-box GPS attacks, demonstrating that even without access to the system model, attackers can cause significant deviations in the UAV's trajectory. This work underscores the importance of developing robust countermeasures and secure control systems to protect against such threats. The use of deep learning-based approaches for optimal false data injection attack scheduling and countermeasure design is a promising direction, as evidenced by the successful implementation on state-of-the-art quadrotors.
Noteworthy Papers
- Intent Modeling and Inference Framework: The introduction of a deep-learning-based intent inference framework using attention-based Bi-LSTM networks is particularly innovative.
- Cyber Security in Operational Technology: The combination of curriculum learning and action masking to enhance data efficiency in cyber security simulations is noteworthy.
- Secure Offloading in MEC Systems: The development of a secure offloading scheme for NOMA-based UAV-MEC systems using deep deterministic policy gradient algorithms is a significant advancement.
- Resilient Location Sharing Services: The FlySafe service, which achieves high spatial awareness and resilience to false location injections, is a notable contribution.
- Security Against Stealthy Attacks: The analysis of stealthy GPS attacks on UAVs and the development of deep learning-based countermeasures are particularly impactful.
