Cyber Risk Assessment and Scoring Model for Small Unmanned Aerial Vehicles

The commercial-off-the-shelf small Unmanned Aerial Vehicle (UAV) market is expanding rapidly in response to interest from hobbyists, commercial businesses, and military operators. The core commercial mission set directly relates to many current military requirements and strategies, with a priority on short range, low cost, real time aerial imaging, and limited modular payloads. These small vehicles present small radar cross sections, low heat signatures, and carry a variety of sensors and payloads. As with many new technologies, security seems secondary to the goal of reaching the market as soon as innovation is viable. Research indicates a growth in exploits and vulnerabilities applicable to small UAV systems, from individual UAV guidance and autopilot controls to the mobile ground station devices that may be as simple as a cellphone application controlling several aircraft. Even if developers strive to improve the security of small UAVs, consumers are left without meaningful insight into the hardware and software protections installed when buying these systems. To date, there is no marketed or accredited risk index for small UAVs. Building from similar domains of aircraft operation, information technologies, cyber-physical systems, and cyber insurance, a cyber risk assessment methodology tailored for small UAVs is proposed and presented in this research. Through case studies of popular models and tailored mission-environment scenarios, the assessment is shown to meet the three objectives of ease-of-use, breadth, and readability. By allowing a cyber risk assessment at or before acquisition, organizations and individuals will be able to accurately compare and choose the best aircraft for their mission

Verification of Localization via Blockchain Technology on Unmanned Aerial Vehicle Swarm

Verification of the geographic location of a moving device is vital. This verification is important in terms of ensuring that the flying systems moving in the swarm are in orbit and that they are able to task completion and manage their energy efficiency. Cyber-attacks on unmanned aerial vehicles (UAV) in a swarm can affect their position and cause various damages. In order to avoid this challenge, it is necessary to share with each other the positions of UAV in the swarm and to increase their accuracy. In this study, it is aimed to increase position accuracy and data integrity of UAV by using blockchain technology in swarm. Experiments were conducted on a virtual UAV network (UAVNet). Successful results were obtained from this proposed study

Intrusion Detection Systems for Flying Ad-hoc Networks

Unmanned Aerial Vehicles (UAVs) are becoming more dependent on mission success than ever. Due to their increase in demand, addressing security vulnerabilities to both UAVs and the Flying Ad-hoc Networks (FANET) they form is more important than ever. As the network traffic is communicated through open airwaves, this network of UAVs relies on monitoring applications known as Intrusion Detection Systems (IDS) to detect and mitigate attacks. This paper will survey current IDS systems that include machine learning techniques when combating various vulnerabilities and attacks from bad actors. This paper will be concluded with research challenges and future research directions in finding an effective IDS system that can handle cyber-attacks while meeting performance requirements.Comment: 5 Pages, 1 figure, 1 table, 41 Reference

A review of cyber threats and defence approaches in emergency management

Emergency planners, first responders and relief workers increasingly rely on computational and communication systems that support all aspects of emergency management, from mitigation and preparedness to response and recovery. Failure of these systems, whether accidental or because of malicious action, can have severe implications for emergency management. Accidental failures have been extensively documented in the past and significant effort has been put into the development and introduction of more resilient technologies. At the same time researchers have been raising concerns about the potential of cyber attacks to cause physical disasters or to maximise the impact of one by intentionally impeding the work of the emergency services. Here, we provide a review of current research on the cyber threats to communication, sensing, information management and vehicular technologies used in emergency management. We emphasise on open issues for research, which are the cyber threats that have the potential to affect emergency management severely and for which solutions have not yet been proposed in the literature

A systematic literature review on Security of Unmanned Aerial Vehicle Systems

Unmanned aerial vehicles (UAVs) are becoming more common, and their operational range is expanding tremendously, making the security aspect of the inquiry essential. This study does a thorough assessment of the literature to determine the most common cyberattacks and the effects they have on UAV assaults on civilian targets. The STRIDE assault paradigm, the challenge they present, and the proper tools for the attack are used to categorize the cyber dangers discussed in this paper. Spoofing and denial of service assaults are the most prevalent types of UAV cyberattacks and have the best results. No attack style demands the employment of a hard-to-reach gadget, indicating that the security environment currently necessitates improvements to UAV use in civilian applications.Comment: 10 Pages, 4 Figure

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

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations