As the fastest growing segment of aviation, unmanned aerial systems (UAS) continue to increase in number, technical complexity and capabilities. Numerous civilian and commercial uses are drastically transforming civil protection, asset delivery, commercial and entertaining activities. However, UAS pose significant challenges in terms of safety, security and privacy within society. An increasing phenomenon, nowadays, is drone-related incidents near airport facilities, which are expected to proliferate in frequency, complexity and severity, as drones become larger and more powerful. Critical infrastructures need to be protected from such aerial attacks, through effective counteracting technologies, risk management and resilience plans. In this paper, we present a survey of drone incidents near airports and a literature review of sensor technologies, able to prevent, detect, identify and mitigate rogue drones. We exhibit the benefits and limitations of available counter-drone technologies (C-UAS); however, defending airports against misused drone activity is a hard problem. Therefore, we analyze three realistic attack scenarios from malicious drones and propose an effective C-UAS protection plan for each case. We discuss applicability limitations of C-UAS in the aviation context and propose a resilience action plan for airport stakeholders for defending against airborne threats from misused drones.

https://www.mdpi.com/1424-8220/20/12/3537/pdf

Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Technologies.

Due to cost reduction and device miniaturization, amateur drones are now widely used in numerous civilian and commercial applications. However, the abuse of amateur drones has resulted in emerging threats to personal privacy and public security. To alleviate these threats, we design an acoustic-based surveillance system, which can achieve the capacity of amateur drones detection and localization with 24/7 (24 hours per day and 7 days per week under normal circumstances) availability. In the designed system, a detection fusion algorithm and a TDOA estimation algorithm based on the Bayesian filter are applied to improve the performance of drone detection and localization. Field experiments are carried out, and the results demonstrate that the designed system can detect and locate an amateur drone in real time with high accuracy and 24/7 availability.

An Acoustic-Based Surveillance System for Amateur Drones Detection and Localization

As the number of drones increases and the era in which they begin to fill the skies approaches, an important question needs to be answered: From a security and privacy perspective, are society and drones really prepared to handle the challenges that a large volume of flights will create? In this paper, we investigate security and privacy in the age of commercial drones. First, we focus on the research question: Are drones and their ecosystems protected against attacks performed by malicious entities? We list a drone’s targets, present a methodology for reviewing attack and countermeasure methods, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Then, we focus on the research question: Is society protected against attacks conducted using drones? We list targets within society, profile the adversaries, review threats, present a methodology for reviewing countermeasures, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Finally, we focus on the primary research question: From the security and privacy perspective, are society and drones prepared to take their relationship one step further? Our analysis reveals that the technological means required to protect drones and society from one another has not yet been developed, and there is a tradeoff between the security and privacy of drones and that of society. That is, the level of security and privacy cannot be optimized concurrently for both entities, because the security and privacy of drones cannot be optimized without decreasing the security and privacy of society, and vice versa.

SoK: Security and Privacy in the Age of Commercial Drones

The evolution of drone technology in the past nine years since the first commercial drone was introduced at CES 2010 has caused many individuals and businesses to adopt drones for various purposes. We are currently living in an era in which drones are being used for pizza delivery, the shipment of goods, and filming, and they are likely to provide an alternative for transportation in the near future. However, drones also pose a significant challenge in terms of security and privacy within society (for both individuals and organizations), and many drone related incidents are reported on a daily basis. These incidents have called attention to the need to detect and disable drones used for malicious purposes and opened up a new area of research and development for academia and industry, with a market that is expected to reach $1.85 billion by 2024. While some of the knowledge used to detect UAVs has been adopted for drone detection, new methods have been suggested by industry and academia alike to deal with the challenges associated with detecting the very small and fast flying objects. In this paper, we describe new societal threats to security and privacy created by drones, and present academic and industrial methods used to detect and disable drones. We review methods targeted at areas that restrict drone flights and analyze their effectiveness with regard to various factors (e.g., weather, birds, ambient light, etc.). We present the challenges arising in areas that allow drone flights, introduce the methods that exist for dealing with these challenges, and discuss the scientific gaps that exist in this area. Finally, we review methods used to disable drones, analyze their effectiveness, and present their expected results. Finally, we suggest future research directions.

SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps.

Spoofing of Unmanned Aerial Vehicles (UAV) is generally carried out through spoofing of the UAV’s Global Positioning System (GPS) receiver. This paper presents a vision-based UAV spoofing detection method that utilizes Visual Odometry (VO). This method is independent of the other complementary sensors and any knowledge or archived map and datasets. The proposed method is based on the comparison of relative sub-trajectory of the UAV from VO, with its absolute replica from GPS within a moving window along the flight path. The comparison is done using three dissimilarity measures including (1) Sum of Euclidian Distances between Corresponding Points (SEDCP), (2) angle distance and (3) taxicab distance between the Histogram of Oriented Displacements (HOD) of these sub-trajectories. This method can determine the time and location of UAV spoofing and bounds the drift error of VO. It can be used without any restriction in the usage environment and can be implemented in real-time applications. This method is evaluated on four UAV spoofing scenarios. The results indicate that this method is effective in the detection of UAV spoofing due to the Sophisticated Receiver-Based (SRB) GPS spoofing. This method can detect UAV spoofing in the long-range UAV flights when the changes in UAV flight direction is larger than 3° and in the incremental UAV spoofing with the redirection rate of 1°. Additionally, using SEDCP, the spoofing of the UAV, when there is no redirection and only the velocity of the UAV is changed, can be detected. The results show that SEDCP is more effective in the detection of UAV spoofing and fake GPS positions.

Spoofing Detection of Civilian UAVs Using Visual Odometry

As a result of continuous cost reduction and device miniaturization, small UAVs are now more easily accessible to the public. Consequently, numerous new applications in the civilian and commercial domains have emerged. However, despite regulations, non-cooperative UAVs have started to abuse low-altitude airspace with potential security and safety problems. In this work, we present a new GNSS spoofing based counter- UAV defense system, which is able to flexibly, friendly, and remotely control a non-cooperating UAV to fly to a location we specify for capture. Our simulation and field study show the effectiveness of such a defense technique.

A Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles

With the growing popularity of mobile devices that have built-in cameras, capturing images has become a trivial job for ordinary people, who share the images with their friends or the public online. However, such digital images are often taken without the consent of some photographed persons, hence leading to privacy concerns. In this paper, we propose iRyP, a purely edge-based privacy-respecting system for mobile cameras. In order to meet the requirements of efficiency and usability, we propose to piggyback privacy policies in the advertising messages of Bluetooth Low Energy (BLE), which has been widely deployed in most mobile devices. As such, privacy policies of people in a photo view can be delivered timely and automatically. Moreover, we propose to use a perceptual hashing algorithm for fast face matching. To improve detection accuracy, we also design several new techniques for face-related image processing. We implement and evaluate a prototype system purely based on the Android platform. Our experiments show that iRyP can meet our design requirements and is practical and ready to use.

iRyP: a purely edge-based visual privacy-respecting system for mobile cameras

The cover image is based on the Letter to Editor Characterization of DNA damage response deficiency in pancreatic cancer patients from China by Liwei Wang et al., https://doi.org/10.1002/cac2.12238. 

Shiqing Chen

Papers

A Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles

iRyP: a purely edge-based visual privacy-respecting system for mobile cameras

Cover Image, Volume 42, Issue 1