An Integrated Framework for Sensing Radio Frequency Spectrum Attacks on Medical Delivery Drones

Drone susceptibility to jamming or spoofing attacks of GPS, RF, Wi-Fi, and operator signals presents a danger to future medical delivery systems. A detection framework capable of sensing attacks on drones could provide the capability for active responses. The identification of interference attacks has applicability in medical delivery, disaster zone relief, and FAA enforcement against illegal jamming activities. A gap exists in the literature for solo or swarm-based drones to identify radio frequency spectrum attacks. Any non-delivery specific function, such as attack sensing, added to a drone involves a weight increase and additional complexity; therefore, the value must exceed the disadvantages. Medical delivery, high-value cargo, and disaster zone applications could present a value proposition which overcomes the additional costs. The paper examines types of attacks against drones and describes a framework for designing an attack detection system with active response capabilities for improving the reliability of delivery and other medical applications.Comment: 7 pages, 1 figures, 5 table

Performance Comparison Of Weak And Strong Learners In Detecting GPS Spoofing Attacks On Unmanned Aerial Vehicles (uavs)

Unmanned Aerial Vehicle systems (UAVs) are widely used in civil and military applications. These systems rely on trustworthy connections with various nodes in their network to conduct their safe operations and return-to-home. These entities consist of other aircrafts, ground control facilities, air traffic control facilities, and satellite navigation systems. Global positioning systems (GPS) play a significant role in UAV\u27s communication with different nodes, navigation, and positioning tasks. However, due to the unencrypted nature of the GPS signals, these vehicles are prone to several cyberattacks, including GPS meaconing, GPS spoofing, and jamming. Therefore, this thesis aims at conducting a detailed comparison of two widely used machine learning techniques, namely weak and strong learners, to investigate their performance in detecting GPS spoofing attacks that target UAVs. Real data are used to generate training datasets and test the effectiveness of machine learning techniques. Various features are derived from this data. To evaluate the performance of the models, seven different evaluation metrics, including accuracy, probabilities of detection and misdetection, probability of false alarm, processing time, prediction time per sample, and memory size, are implemented. The results show that both types of machine learning algorithms provide high detection and low false alarm probabilities. In addition, despite being structurally weaker than strong learners, weak learner classifiers also, achieve a good detection rate. However, the strong learners slightly outperform the weak learner classifiers in terms of multiple evaluation metrics, including accuracy, probabilities of misdetection and false alarm, while weak learner classifiers outperform in terms of time performance metrics

Risk driven models & security framework for drone operation in GNSS-denied environments

Flying machines in the air without human inhabitation has moved from abstracts to reality and the concept of unmanned aerial vehicles continues to evolve. Drones are popularly known to use GPS and other forms of GNSS for navigation, but this has unfortunately opened them up to spoofing and other forms of cybersecurity threats. The use of computer vision to find location through pre-stored satellite images has become a suggested solution but this gives rise to security challenges in the form of spoofing, tampering, denial of service and other forms of attacks. These security challenges are reviewed with appropriate requirements recommended. This research uses the STRIDE threat analysis model to analyse threats in drone operation in GNSS-denied environment. Other threat models were considered including DREAD and PASTA, but STRIDE is chosen because of its suitability and the complementary ability it serves to other analytical methods used in this work. Research work is taken further to divide the drone system into units based in similarities in functions and architecture. They are then subjected to Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA). The STRIDE threat model is used as base events for the FTA and an FMEA is conducted based on adaptations from IEC 62443-1-1, Network and System Security- Terminology, concepts, and models and IEC 62443-3-2, security risk assessment for system design. The FTA and FMEA are widely known for functional safety purposes but there is a divergent use for the tools where we consider cybersecurity vulnerabilities specifically, instead of faults. The IEC 62443 series has become synonymous with Industrial Automation and Control Systems. However, inspiration is drawn from that series for this work because, drones, as much as any technological gadget in play recently, falls under a growing umbrella of quickly evolving devices, known as Internet of Things (IoT). These IoT devices can be principally considered as part of Industrial Automation and Control Systems. Results from the analysis are used to recommend security standards & requirements that can be applied in drone operation in GNSS-denied environments. The framework recommended in this research is consistent with IEC 62443-3-3, System security requirements and security levels and has the following categorization from IEC 62443-1-1, identification, and authentication control, use control, system integrity, data confidentiality, restricted data flow, timely response to events and resource availability. The recommended framework is applicable and relevant to military, private and commercial drone deployment because the framework can be adapted and further tweaked to suit the context which it is intended for. Application of this framework in drone operation in GNSS denied environment will greatly improve upon the cyber resilience of the drone network system